International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Personalizing Image Search from the Photo Sharing WebsitesAM Publications
Abstract: Increasingly developed social sharing websites, like Flickr and Youtube, allow users to create, share, and
annotate a comment Medias. The large-scale user-generated meta-data not only facilitate users in sharing and
organizing multimedia content, but provide useful information to improve media retrieval and management.
Personalized search serves as one of such examples where the web search experience is improved by generating the
returned list according to the modified user search intents. In this paper, we exploit the social annotations and
propose a novel framework simultaneously considering the user and query relevance to learn to personalized image
search. The basic premise is to embed the user preference and query-related search intent into user-specific topic
spaces. Since the users’ original annotation is too sparse for topic modeling, we need to enrich users’ annotation pool
before user-specific topic spaces construction.
The proposed framework contains two components:
1) A Ranking based Multi-correlation Tensor Factorization model is proposed to perform annotation prediction,
which is considered as users’ potential annotations for the images;
2) We introduce User-specific Topic Modeling to map the query relevance and user preference into the same userspecific
topic space. For performance evaluation, two resources involved with users’ social activities are employed.
Experiments on a large-scale Flickr dataset demonstrate the effectiveness of the proposed method.
Personalized geo tag recommendation for community contributed imageseSAT Journals
Abstract Tagging is popularized by many social sharing websites, which allows us to add the description to object. Using tags users can organize their data so that it will be helpful for searching and browsing. Geotagging of a photo is the process in which a photo is marked with the geographical identification of the place it was taken. Geotagging can benefit users, to discover an Extensive Variation of exact Location related information. In personalized tag recommendation, tags that are relevant to the user's query are retrieved based upon the user's interest. The introduction of the Hypergraph learning is to find joint relevance between the visual and textual domains. Given a photo with Geolocation and without tags, System uses nearest neighbour search to obtain some user- predilected tags and geo-location predilected tags individually. It discovers the semantically and visually related images, and explores the idea of annotation-by-search to recommend tags for the untagged photo. In conclusion, the tags are recommended to the user. Keywords: Hypergraph Construction, Hypergraph learning, Personalization, Geo-tags, Preference learning
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
Adaptive Search Based On User Tags in Social NetworkingIOSR Journals
This document summarizes an article about adaptive search based on user tags in social networking. It discusses using tags that users apply to images in social media sites like Flickr to improve image search and personalize results. It proposes using topic models to identify different meanings of ambiguous tags and a user's interests to display more relevant images. The framework involves reranking images based on aesthetics scores predicted from user comments, and using tag-based and group-based metadata to discover topics and personalize search results. Future work could further analyze community-generated metadata to identify interests and refine search algorithms.
Tag based image retrieval (tbir) using automatic image annotationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper on developing user profiles from search engine queries to enable personalized search results. It discusses how current search engines generally return the same results regardless of individual user interests. The paper proposes methods to construct user profiles capturing both positive and negative preferences from search histories and click-through data. Experimental results showed profiles including both preferences performed best by improving query clustering and separating similar vs. dissimilar queries. Future work aims to use profiles for collaborative filtering and predicting new query intents.
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Social Re-Ranking using Tag Based Image SearchIRJET Journal
This document proposes a social re-ranking system for tag-based image retrieval from social media datasets. It first retrieves images based on keyword matching of the query tags. It then applies three re-ranking steps: 1) Inter-user ranking to rank images by user contributions to the query, 2) Time stamp ranking to prioritize more recent images based on title and timestamp, 3) View ranking to improve relevance by considering image view counts. An experiment on a social image dataset showed the method effectively and efficiently retrieves more relevant and diverse images compared to other tag-based image ranking methods.
Personalizing Image Search from the Photo Sharing WebsitesAM Publications
Abstract: Increasingly developed social sharing websites, like Flickr and Youtube, allow users to create, share, and
annotate a comment Medias. The large-scale user-generated meta-data not only facilitate users in sharing and
organizing multimedia content, but provide useful information to improve media retrieval and management.
Personalized search serves as one of such examples where the web search experience is improved by generating the
returned list according to the modified user search intents. In this paper, we exploit the social annotations and
propose a novel framework simultaneously considering the user and query relevance to learn to personalized image
search. The basic premise is to embed the user preference and query-related search intent into user-specific topic
spaces. Since the users’ original annotation is too sparse for topic modeling, we need to enrich users’ annotation pool
before user-specific topic spaces construction.
The proposed framework contains two components:
1) A Ranking based Multi-correlation Tensor Factorization model is proposed to perform annotation prediction,
which is considered as users’ potential annotations for the images;
2) We introduce User-specific Topic Modeling to map the query relevance and user preference into the same userspecific
topic space. For performance evaluation, two resources involved with users’ social activities are employed.
Experiments on a large-scale Flickr dataset demonstrate the effectiveness of the proposed method.
Personalized geo tag recommendation for community contributed imageseSAT Journals
Abstract Tagging is popularized by many social sharing websites, which allows us to add the description to object. Using tags users can organize their data so that it will be helpful for searching and browsing. Geotagging of a photo is the process in which a photo is marked with the geographical identification of the place it was taken. Geotagging can benefit users, to discover an Extensive Variation of exact Location related information. In personalized tag recommendation, tags that are relevant to the user's query are retrieved based upon the user's interest. The introduction of the Hypergraph learning is to find joint relevance between the visual and textual domains. Given a photo with Geolocation and without tags, System uses nearest neighbour search to obtain some user- predilected tags and geo-location predilected tags individually. It discovers the semantically and visually related images, and explores the idea of annotation-by-search to recommend tags for the untagged photo. In conclusion, the tags are recommended to the user. Keywords: Hypergraph Construction, Hypergraph learning, Personalization, Geo-tags, Preference learning
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
Adaptive Search Based On User Tags in Social NetworkingIOSR Journals
This document summarizes an article about adaptive search based on user tags in social networking. It discusses using tags that users apply to images in social media sites like Flickr to improve image search and personalize results. It proposes using topic models to identify different meanings of ambiguous tags and a user's interests to display more relevant images. The framework involves reranking images based on aesthetics scores predicted from user comments, and using tag-based and group-based metadata to discover topics and personalize search results. Future work could further analyze community-generated metadata to identify interests and refine search algorithms.
Tag based image retrieval (tbir) using automatic image annotationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper on developing user profiles from search engine queries to enable personalized search results. It discusses how current search engines generally return the same results regardless of individual user interests. The paper proposes methods to construct user profiles capturing both positive and negative preferences from search histories and click-through data. Experimental results showed profiles including both preferences performed best by improving query clustering and separating similar vs. dissimilar queries. Future work aims to use profiles for collaborative filtering and predicting new query intents.
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
Social Re-Ranking using Tag Based Image SearchIRJET Journal
This document proposes a social re-ranking system for tag-based image retrieval from social media datasets. It first retrieves images based on keyword matching of the query tags. It then applies three re-ranking steps: 1) Inter-user ranking to rank images by user contributions to the query, 2) Time stamp ranking to prioritize more recent images based on title and timestamp, 3) View ranking to improve relevance by considering image view counts. An experiment on a social image dataset showed the method effectively and efficiently retrieves more relevant and diverse images compared to other tag-based image ranking methods.
Attribute Based Image Duplication Alert Message Using Big DataIJERA Editor
Using this paper “Attribute Based Image Duplication Alert Message Using Big Data” the stranger cannot copy any of the photo which is related to the user and, we also provide fast image search. That means, the image will be searched by using the attribute name and the hyper graph learning method. The user will have a global vision on searching the photos. It will be visible and more easily findable. Users can search more within a short period. It mainly provides users privacy. We proposed a new attribute-assisted method based on hyper graph learning. First we have to train several classifiers for all the pre-defined. Attributes and each image is represented by attribute feature consisting of the responses from these classifiers. Semantic attributes are expected to narrow down the semantic gap between low-level visual features and high level semantic meanings. If any naïve user is trying to copy the images, Automatic message can be send to the users using Rabbit MQ when any stranger copy the image.
The document summarizes a research paper that proposes a personalized recommendation approach combining social network factors like interpersonal interest similarity and interpersonal rating behavior similarity. It uses probabilistic matrix factorization to predict ratings by considering these social network factors. The approach is evaluated on two large real-world social rating datasets and shows improved performance over approaches that only use social network information.
Dear Students
Ingenious techno Solution offers an expertise guidance on you Final Year IEEE & Non- IEEE Projects on the following domain
JAVA
.NET
EMBEDDED SYSTEMS
ROBOTICS
MECHANICAL
MATLAB etc
For further details contact us:
enquiry@ingenioustech.in
044-42046028 or 8428302179.
Ingenious Techno Solution
#241/85, 4th floor
Rangarajapuram main road,
Kodambakkam (Power House)
http://www.ingenioustech.in/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Stabilization of Black Cotton Soil with Red Mud and Formulation of Linear Reg...IRJET Journal
This document describes a proposed friend discovery system for online social networks that recommends friends to users based on their lifestyles, behaviors, ratings, profile analyses, and comments rather than just location. It uses a predefined form for users to indicate their daily activities to better determine lifestyle similarities. The system also provides security using AES encryption algorithms. The proposed system aims to address limitations of existing systems that rely only on social graphs or unstructured lifestyle data from users.
Social search interfaces aim to improve information seeking through collaboration. Three studies evaluated FeedMe, SearchTogether, and Coagmento. FeedMe allowed one-click link sharing but raised privacy concerns without public knowledge triggers. SearchTogether supported large group collaboration but users wanted more real-time features. Coagmento effectively supported group awareness but received low marks for personal awareness. Overall, more research is needed on privacy protections within social search interfaces to fully realize their benefits while respecting user privacy.
Optimization of Image Search from Photo Sharing Websitesijceronline
The document proposes a system for personalized image search from photo sharing websites. The system has two main components: 1) A Ranking Based Multi-correlation Tensor Factorization model (RMTF) is used to calculate a user's preferences for annotating images based on their past tags. 2) A User Specific Topic Modeling (USTM) approach uses LDA to generate topics specific to each user based on the images and tags. When a user submits a query, the system maps it to relevant user topics and ranks results based on the topic-sensitive user preferences calculated by RMTF and USTM to provide a personalized set of images. The system aims to improve upon keyword-based search by considering individual user intentions and histories to
This document proposes a content-based image search engine that can retrieve relevant images from a large database based on a user-input query image. It discusses how content-based image retrieval systems work by extracting visual features like color, texture and shape from images. The proposed search engine would detect faces in input images using OpenCV and compare visual features to find matching images in the database. It would then retrieve and display the related information and images to the user. The goal is to build a more accurate image search compared to traditional text-based search engines by analyzing visual content of images.
Nowadays Social Media focus on users to billions of images, famous e commerce web sites such as Flipkart, Amazon etc. Tag-primarily based definitely image are seeking for is an essential method to find photos shared with the aid of manner of clients in social networks. But, a manner to make the top ranked result applicable and with range is difficult. In this paper, we advocate a topic diverse ranking method for tag-primarily based photo retrieval with the eye of selling the situation insurance overall performance. First, we bring together a tag graph based totally absolutely at the similarity among every tag. Then network detection approach is accomplished to mine the subject community of every tag. After that, inter-community and intra-network ranking are added to gather the very last retrieved results. Inside the inter-network rating way, an adaptive random stroll model is employed to rank the network primarily based at the multi-information of every topic community D. Dhayalan | M. Queen Mary Vidya | B. Gowri Priya "Image Tagging With Social Assistance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Active Galaxy , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14566.pdf
http://www.ijtsrd.com/engineering/computer-engineering/14566/image-tagging-with-social-assistance/d-dhayalan
iaetsd Adaptive privacy policy prediction for user uploaded images onIaetsd Iaetsd
This document proposes an Adaptive Privacy Policy Prediction (A3P) system to help users set privacy policies for images uploaded to social media sites. The system includes two main components: A3P-Core and A3P-Social. A3P-Core classifies images and predicts policies based on image content and user history. A3P-Social predicts policies based on a user's social context when A3P-Core cannot, such as for new users. It groups users with similar social attributes and privacy preferences to identify communities to reference for policy recommendations. The system was evaluated against alternative approaches and showed improved accuracy in predicting privacy policies for uploaded images.
This document summarizes a research paper that proposes a method for privacy-preserving friend matching and recommendation in social networks. It analyzes user data like interests from social profiles to determine dominant lifestyle vectors using LDA (Latent Dirichlet Allocation). Similarities between users' lifestyle vectors are calculated using cosine and distance similarity. Friends are recommended to a user if their similarity score exceeds a threshold. The proposed system creates an interface for users to log in, analyzes user activities to determine dominant lifestyles, and recommends potential friends with similar interests based on lifestyle vector similarities.
A Proposal on Social Tagging Systems Using Tensor Reduction and Controlling R...ijcsa
Social Tagging System is the process in which user makes their interest by tagging on a particular item. These STS are in associated with web 2.0 and has sourceful information for the users with their recommendations. It provides different types of recommendations are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the KernelSVD smoothing technique. We provide now with the 4-order tensor approach, which we named as Tensor Reduction. Here the items that are tagged can be viewed by the user who are recommended the same item and tagged over it. There by can improve the social tagging recommendations efficiency and also the unwanted request has been controlled. The results show significant improvements in terms of effectiveness.
This document discusses techniques for personalizing search engine results using concept-based user profiles. It proposes six methods for creating user profiles that capture both positive and negative user preferences and interests based on concepts extracted from search queries and results. The methods use machine learning algorithms to learn weighted concept vectors representing user profiles. An evaluation found that profiles capturing both positive and negative preferences performed best. The goal is to resolve query ambiguity and increase result relevance by understanding each user's unique interests and preferences.
Projection Multi Scale Hashing Keyword Search in Multidimensional DatasetsIRJET Journal
The document discusses a novel method called ProMiSH (Projection and Multi Scale Hashing) for keyword search in multi-dimensional datasets. ProMiSH uses random projection and hash-based index structures to achieve high scalability and speedup of more than four orders over state-of-the-art tree-based techniques. Empirical studies on real and synthetic datasets of sizes up to 10 million objects and 100 dimensions show ProMiSH scales linearly with dataset size, dimension, query size, and result size. The method groups objects embedded in a vector space that are tagged with keywords matching a given query.
PRIVACY POLICY INFERENCE OF USER-UPLOADED IMAGES ON CONTENT SHARING SITESShakas Technologies
This document describes a proposed system called Adaptive Privacy Policy Prediction (A3P) that aims to automatically generate personalized privacy policies for images uploaded by users to social media sites. The system considers several factors that may influence privacy preferences, including social context, image content, metadata, and changes in individual privacy attitudes over time. It uses a two-stage approach involving image classification and adaptive policy prediction. Images are first classified based on content and metadata, then policies for similar images are analyzed to predict policies for new images. The system is evaluated on over 5,000 policies with over 90% accuracy.
11. Efficient Image Based Searching for Improving User Search Image GoalsINFOGAIN PUBLICATION
The analysis of a user search goals for a query can be very useful in improving search engine relevance and the user experience. Although the research on inferring by user goals and intents for text search has received much attention, so small has been proposed for image search. In this paper, we propose to leverage click session information, which will indicate by high correlations among the clicked images in a session in a user click-through logs, and combine it with the clicked image visual information for inferring the user image-search goals. Since the click session information can serve as past users’ implicit guidance for the clustering the images, more precise user search goals can be obtained. The two strategies are proposed because of combine image visual information for the click session information. Furthermore a classification risk based on approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on the popular commercial search engine for demonstrate the effectiveness of the proposed method
Socially Shared Images with Automated Annotation Process by Using Improved Us...IJERA Editor
Objectives: The main objective of this research is to increase the semantic concepts prominently as well as
reduce the searching time complexity. This is also aimed to ensure the higher privacy with security and develop
the accurate privacy policy generation.
Methods: The existing method named as adaptive privacy policy prediction (A3P) is used to discover the best
available privacy policy for the user’s image being uploaded. The proposed method name as improved semantic
annotated markovian semantic Indexing (ISMSI) is used for retrieving the images semantically.
Findings: The proposed method achieves high performance in terms of greater accuracy values.
Application/Improvements: The proposed system is done by using semantic annotated markovian semantic
Indexing (ISMSI) approach. ISMSI method is used for identification of similarity as well as semantic annotated
images and improves the privacy significantly.
User search goal inference and feedback session using fast generalized – fuzz...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A tutorial review of automatic image tagging technique using text miningeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
EAD: Antes e depois da cibercultura
Trabalho de educação a distãncia- CEDERJ
Aline Corrêa, Débora Vieira de Almeida e Celma Brum de Arruda
Pedagogia- 7º período - Nova Iguaçu
La teoría de las relaciones humanas surgió como reacción a la teoría clásica de la administración, la cual era vista como deshumanizante. El experimento de Hawthorne mostró que factores psicológicos y sociales influyen en la productividad más que factores físicos. Elton Mayo concluyó que los trabajadores se sienten más satisfechos e integrados cuando forman parte de pequeños grupos y que su productividad depende de esta integración social.
Attribute Based Image Duplication Alert Message Using Big DataIJERA Editor
Using this paper “Attribute Based Image Duplication Alert Message Using Big Data” the stranger cannot copy any of the photo which is related to the user and, we also provide fast image search. That means, the image will be searched by using the attribute name and the hyper graph learning method. The user will have a global vision on searching the photos. It will be visible and more easily findable. Users can search more within a short period. It mainly provides users privacy. We proposed a new attribute-assisted method based on hyper graph learning. First we have to train several classifiers for all the pre-defined. Attributes and each image is represented by attribute feature consisting of the responses from these classifiers. Semantic attributes are expected to narrow down the semantic gap between low-level visual features and high level semantic meanings. If any naïve user is trying to copy the images, Automatic message can be send to the users using Rabbit MQ when any stranger copy the image.
The document summarizes a research paper that proposes a personalized recommendation approach combining social network factors like interpersonal interest similarity and interpersonal rating behavior similarity. It uses probabilistic matrix factorization to predict ratings by considering these social network factors. The approach is evaluated on two large real-world social rating datasets and shows improved performance over approaches that only use social network information.
Dear Students
Ingenious techno Solution offers an expertise guidance on you Final Year IEEE & Non- IEEE Projects on the following domain
JAVA
.NET
EMBEDDED SYSTEMS
ROBOTICS
MECHANICAL
MATLAB etc
For further details contact us:
enquiry@ingenioustech.in
044-42046028 or 8428302179.
Ingenious Techno Solution
#241/85, 4th floor
Rangarajapuram main road,
Kodambakkam (Power House)
http://www.ingenioustech.in/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Stabilization of Black Cotton Soil with Red Mud and Formulation of Linear Reg...IRJET Journal
This document describes a proposed friend discovery system for online social networks that recommends friends to users based on their lifestyles, behaviors, ratings, profile analyses, and comments rather than just location. It uses a predefined form for users to indicate their daily activities to better determine lifestyle similarities. The system also provides security using AES encryption algorithms. The proposed system aims to address limitations of existing systems that rely only on social graphs or unstructured lifestyle data from users.
Social search interfaces aim to improve information seeking through collaboration. Three studies evaluated FeedMe, SearchTogether, and Coagmento. FeedMe allowed one-click link sharing but raised privacy concerns without public knowledge triggers. SearchTogether supported large group collaboration but users wanted more real-time features. Coagmento effectively supported group awareness but received low marks for personal awareness. Overall, more research is needed on privacy protections within social search interfaces to fully realize their benefits while respecting user privacy.
Optimization of Image Search from Photo Sharing Websitesijceronline
The document proposes a system for personalized image search from photo sharing websites. The system has two main components: 1) A Ranking Based Multi-correlation Tensor Factorization model (RMTF) is used to calculate a user's preferences for annotating images based on their past tags. 2) A User Specific Topic Modeling (USTM) approach uses LDA to generate topics specific to each user based on the images and tags. When a user submits a query, the system maps it to relevant user topics and ranks results based on the topic-sensitive user preferences calculated by RMTF and USTM to provide a personalized set of images. The system aims to improve upon keyword-based search by considering individual user intentions and histories to
This document proposes a content-based image search engine that can retrieve relevant images from a large database based on a user-input query image. It discusses how content-based image retrieval systems work by extracting visual features like color, texture and shape from images. The proposed search engine would detect faces in input images using OpenCV and compare visual features to find matching images in the database. It would then retrieve and display the related information and images to the user. The goal is to build a more accurate image search compared to traditional text-based search engines by analyzing visual content of images.
Nowadays Social Media focus on users to billions of images, famous e commerce web sites such as Flipkart, Amazon etc. Tag-primarily based definitely image are seeking for is an essential method to find photos shared with the aid of manner of clients in social networks. But, a manner to make the top ranked result applicable and with range is difficult. In this paper, we advocate a topic diverse ranking method for tag-primarily based photo retrieval with the eye of selling the situation insurance overall performance. First, we bring together a tag graph based totally absolutely at the similarity among every tag. Then network detection approach is accomplished to mine the subject community of every tag. After that, inter-community and intra-network ranking are added to gather the very last retrieved results. Inside the inter-network rating way, an adaptive random stroll model is employed to rank the network primarily based at the multi-information of every topic community D. Dhayalan | M. Queen Mary Vidya | B. Gowri Priya "Image Tagging With Social Assistance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Active Galaxy , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14566.pdf
http://www.ijtsrd.com/engineering/computer-engineering/14566/image-tagging-with-social-assistance/d-dhayalan
iaetsd Adaptive privacy policy prediction for user uploaded images onIaetsd Iaetsd
This document proposes an Adaptive Privacy Policy Prediction (A3P) system to help users set privacy policies for images uploaded to social media sites. The system includes two main components: A3P-Core and A3P-Social. A3P-Core classifies images and predicts policies based on image content and user history. A3P-Social predicts policies based on a user's social context when A3P-Core cannot, such as for new users. It groups users with similar social attributes and privacy preferences to identify communities to reference for policy recommendations. The system was evaluated against alternative approaches and showed improved accuracy in predicting privacy policies for uploaded images.
This document summarizes a research paper that proposes a method for privacy-preserving friend matching and recommendation in social networks. It analyzes user data like interests from social profiles to determine dominant lifestyle vectors using LDA (Latent Dirichlet Allocation). Similarities between users' lifestyle vectors are calculated using cosine and distance similarity. Friends are recommended to a user if their similarity score exceeds a threshold. The proposed system creates an interface for users to log in, analyzes user activities to determine dominant lifestyles, and recommends potential friends with similar interests based on lifestyle vector similarities.
A Proposal on Social Tagging Systems Using Tensor Reduction and Controlling R...ijcsa
Social Tagging System is the process in which user makes their interest by tagging on a particular item. These STS are in associated with web 2.0 and has sourceful information for the users with their recommendations. It provides different types of recommendations are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the KernelSVD smoothing technique. We provide now with the 4-order tensor approach, which we named as Tensor Reduction. Here the items that are tagged can be viewed by the user who are recommended the same item and tagged over it. There by can improve the social tagging recommendations efficiency and also the unwanted request has been controlled. The results show significant improvements in terms of effectiveness.
This document discusses techniques for personalizing search engine results using concept-based user profiles. It proposes six methods for creating user profiles that capture both positive and negative user preferences and interests based on concepts extracted from search queries and results. The methods use machine learning algorithms to learn weighted concept vectors representing user profiles. An evaluation found that profiles capturing both positive and negative preferences performed best. The goal is to resolve query ambiguity and increase result relevance by understanding each user's unique interests and preferences.
Projection Multi Scale Hashing Keyword Search in Multidimensional DatasetsIRJET Journal
The document discusses a novel method called ProMiSH (Projection and Multi Scale Hashing) for keyword search in multi-dimensional datasets. ProMiSH uses random projection and hash-based index structures to achieve high scalability and speedup of more than four orders over state-of-the-art tree-based techniques. Empirical studies on real and synthetic datasets of sizes up to 10 million objects and 100 dimensions show ProMiSH scales linearly with dataset size, dimension, query size, and result size. The method groups objects embedded in a vector space that are tagged with keywords matching a given query.
PRIVACY POLICY INFERENCE OF USER-UPLOADED IMAGES ON CONTENT SHARING SITESShakas Technologies
This document describes a proposed system called Adaptive Privacy Policy Prediction (A3P) that aims to automatically generate personalized privacy policies for images uploaded by users to social media sites. The system considers several factors that may influence privacy preferences, including social context, image content, metadata, and changes in individual privacy attitudes over time. It uses a two-stage approach involving image classification and adaptive policy prediction. Images are first classified based on content and metadata, then policies for similar images are analyzed to predict policies for new images. The system is evaluated on over 5,000 policies with over 90% accuracy.
11. Efficient Image Based Searching for Improving User Search Image GoalsINFOGAIN PUBLICATION
The analysis of a user search goals for a query can be very useful in improving search engine relevance and the user experience. Although the research on inferring by user goals and intents for text search has received much attention, so small has been proposed for image search. In this paper, we propose to leverage click session information, which will indicate by high correlations among the clicked images in a session in a user click-through logs, and combine it with the clicked image visual information for inferring the user image-search goals. Since the click session information can serve as past users’ implicit guidance for the clustering the images, more precise user search goals can be obtained. The two strategies are proposed because of combine image visual information for the click session information. Furthermore a classification risk based on approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on the popular commercial search engine for demonstrate the effectiveness of the proposed method
Socially Shared Images with Automated Annotation Process by Using Improved Us...IJERA Editor
Objectives: The main objective of this research is to increase the semantic concepts prominently as well as
reduce the searching time complexity. This is also aimed to ensure the higher privacy with security and develop
the accurate privacy policy generation.
Methods: The existing method named as adaptive privacy policy prediction (A3P) is used to discover the best
available privacy policy for the user’s image being uploaded. The proposed method name as improved semantic
annotated markovian semantic Indexing (ISMSI) is used for retrieving the images semantically.
Findings: The proposed method achieves high performance in terms of greater accuracy values.
Application/Improvements: The proposed system is done by using semantic annotated markovian semantic
Indexing (ISMSI) approach. ISMSI method is used for identification of similarity as well as semantic annotated
images and improves the privacy significantly.
User search goal inference and feedback session using fast generalized – fuzz...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A tutorial review of automatic image tagging technique using text miningeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
EAD: Antes e depois da cibercultura
Trabalho de educação a distãncia- CEDERJ
Aline Corrêa, Débora Vieira de Almeida e Celma Brum de Arruda
Pedagogia- 7º período - Nova Iguaçu
La teoría de las relaciones humanas surgió como reacción a la teoría clásica de la administración, la cual era vista como deshumanizante. El experimento de Hawthorne mostró que factores psicológicos y sociales influyen en la productividad más que factores físicos. Elton Mayo concluyó que los trabajadores se sienten más satisfechos e integrados cuando forman parte de pequeños grupos y que su productividad depende de esta integración social.
This document provides an overview of the invention management process. It defines invention and discusses the nature of invention. It outlines the stages of the invention process from idea generation to prototype testing and modification. It discusses keeping an invention log or notebook to track the invention process. The document also covers inventorship, the invention disclosure process, and using laboratory notebooks as invention disclosures.
El documento presenta recomendaciones para realizar búsquedas efectivas en internet, incluyendo el uso de palabras clave y operadores lógicos. También discute las ventajas y desventajas de las tecnologías de la información y la comunicación en la educación desde las perspectivas de los estudiantes y profesores.
Este documento presenta la información básica sobre las bases de datos. Explica que una base de datos es un conjunto de datos almacenados sistemáticamente para su uso futuro. Describe los tipos de bases de datos según su variabilidad y contenido. También define características clave como datos relacionados e integrados y enumera algunas ventajas y desventajas comunes de las bases de datos.
Este documento describe el planteamiento del problema de investigación sobre la incidencia de la gestión financiera en la creación de valor para las empresas de servicios de salud ocupacional privados en San Cristóbal, Venezuela entre 2010-2012. Explica que estas empresas se han visto perjudicadas por factores como la alta inflación, lo que ha causado un estancamiento en su crecimiento e inversión. El objetivo general es analizar cómo la gestión financiera puede identificar oportunidades de inversión para incrementar el valor de estas empresas durante ese período.
El documento describe la historia y desarrollo de la teoría atómica desde la antigua India hasta John Dalton. Resume las principales hipótesis de Dalton sobre la naturaleza de la materia, incluyendo que los elementos están compuestos de átomos idénticos y que las relaciones químicas implican la combinación y separación de átomos. También explica la estructura básica del átomo con un núcleo central rodeado de electrones.
La teoría de las relaciones humanas surgió en respuesta a los resultados del experimento de Hawthorne que mostraron que los factores psicológicos y sociales afectan la productividad de los trabajadores más que los factores físicos. El experimento encontró que la productividad aumentaba cuando los trabajadores se sentían aceptados socialmente y parte de un grupo. La teoría enfatiza la importancia de las necesidades humanas y sociales en el lugar de trabajo.
El documento describe los pasos para crear una base de datos en Access, agregar una tabla y campos, y luego crear un reporte en Visual Basic para mostrar los datos de la tabla. Se agregan controles de botón para agregar, eliminar y ver registros, y se incluye código para conectarse a la base de datos y realizar las acciones correspondientes a cada botón.
La Propuesta Educativa Multigrado 2005 fue elaborada por la Secretaría de Educación Pública de México en colaboración con docentes, asesores y equipos técnicos para mejorar la enseñanza y el aprendizaje en las escuelas multigrado del país. El documento presenta elementos como adecuaciones curriculares, planeación por tema común, actividades permanentes y sugerencias didácticas para apoyar a los maestros en la atención de dos grados o más en un mismo grupo.
estas son son diapositivas que hablan de 2 enfermedades que usted debe tomar en cueta que es sobre la bulimia y anorexia.
estas enfermedades no son un juego uno tiene que tomar conciencia de que hacer esto es como estar matandose asi mismo
La teoría de las relaciones humanas surgió en respuesta a los resultados del experimento de Hawthorne que mostraron que los factores psicológicos y sociales afectan la productividad de los trabajadores más que los factores físicos. El experimento encontró que la productividad aumentaba cuando los trabajadores se sentían aceptados socialmente y parte de un grupo. La teoría enfatiza la importancia de las necesidades humanas y sociales en el lugar de trabajo.
El documento describe el fenómeno de fatiga del material en tuberías causado por transitorios hidráulicos. La fatiga ocurre cuando una tubería está sujeta a esfuerzos cíclicos repetitivos y puede fallar con presiones más bajas de lo normal. Se presentan curvas que muestran cómo la presión de rotura disminuye con el número de ciclos. También se describe un caso real de falla por fatiga en una válvula. Finalmente, se explican métodos para calcular el espesor necesario de la tubería para proteger
La anorexia nerviosa es un trastorno grave de la imagen corporal que causa un miedo obsesivo a engordar y una distorsión de la percepción del peso y la forma del cuerpo. Sus síntomas incluyen el rechazo a mantener un peso saludable, la amenorrea en mujeres y el miedo intenso a aumentar de peso. El tratamiento busca corregir la malnutrición y resolver los problemas psicológicos del paciente y su familia, normalmente requiriendo aislamiento familiar y la guía de un experto.
El documento describe el proceso de implantación de una cultura de calidad en el CIFP Río Tormes a través de la certificación ISO 9001. Comenzó su camino hacia la calidad en 2003-2004 y ha obtenido varias certificaciones desde entonces. La certificación ha traído mejoras como la documentación del sistema, indicadores para medir resultados y planes de mejora. Se explican los pasos para implantar un sistema de gestión de calidad, resaltando el compromiso de la dirección.
Este documento identifica y explica varios factores de riesgo en el área laboral. Define un factor de riesgo como un elemento que puede afectar negativamente la salud de un trabajador. Luego discute factores ambientales como la temperatura y la humedad, factores derivados de la carga de trabajo física y mental, y concluye que existen muchos factores de riesgo importantes que deben evitarse para proteger la seguridad y salud de los trabajadores.
Esta presentación ilustra la evolución de CM como profesión, además de la posible implementación de una estrategia de promoción para el Postgrado de Mercadeo para Empresas de la UCV.
Trabajo final: redes sociales en entornos educativosNoheli Barrio
Este proyecto busca motivar a los estudiantes de primer año a la lectura y escritura a través del uso de Facebook. El proyecto tendrá cuatro etapas: 1) visitar bibliotecas digitales, 2) seleccionar y comentar libros en Facebook, 3) escribir y editar textos literarios, y 4) crear y compartir presentaciones multimedia de sus escritos en Facebook. El proyecto durará aproximadamente un cuatrimestre y la evaluación considerará la participación y trabajo de los estudiantes.
La teoría de las relaciones humanas surgió en respuesta a los resultados del experimento de Hawthorne que cuestionaron la teoría clásica de la administración. El experimento mostró que factores psicológicos como sentirse aceptado en un grupo y la satisfacción laboral influyen en la productividad más que factores físicos. La teoría de las relaciones humanas enfatiza la importancia de las necesidades sociales y emocionales de los trabajadores y promueve una organización donde los empleados se sientan comprometidos con grupos
Personalized web search using browsing history and domain knowledgeRishikesh Pathak
This document proposes a framework for improving personalized web search by constructing an enhanced user profile using both the user's browsing history and domain knowledge. The enhanced user profile is used to better suggest relevant web pages to the user based on their search query. An experiment found that suggestions made using the enhanced user profile performed better than using a standard user profile alone. The framework involves modeling the user, re-ranking search results, and displaying personalized results based on the enhanced user profile.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Ijricit 01-008 confidentiality strategy deduction of user-uploaded pictures o...Ijripublishers Ijri
With the growing quantity of pictures users distribute from node to node social networks, retaining confidentiality has turn out to be a foremost predicament, as declared by a latest wave of made known occurrences wherever users unintentionally shared individual profile. In radiance of these occurrences made necessitate of tools to assist users organize access to their distributed data is evident. In the direction of speak to this requirement, we suggest an Adaptive Privacy Policy forecast (A3P) scheme to facilitate users compile confidentiality settings for their pictures. We observe the responsibility of communal context, picture content, and metadata as possible sign of users’ confidentiality preference. We recommend a two-level structure which according to the user’s obtainable times past on the site, establishs the most excellent obtainable confidentiality policy for the user’s pictures being uploaded. Our solution relies on an image classification framework for image categories which may be associated with similar policies, and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users’ social features. Over time, the generated policies will follow the evolution of users’ privacy attitude. We provide the results of our extensive evaluation over 5,000 policies, which demonstrate the effectiveness of our system, with prediction accuracies over 90 percent.
This document summarizes an article that proposes a new approach to improving image web search techniques. It begins with an abstract that describes how rapidly increasing internet users and multimedia search are increasing network traffic, with most traffic being for searching multimedia content online. It then provides background on existing issues with image search and commonly used algorithms. The document proposes a hybrid image search engine that searches by image-to-image comparison, text/keywords, and uses a hybrid matches graph and time sensitivity filter to improve results. It describes the training and testing phases, featuring feature extraction and annotation matching to retrieve relevant images from queries.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
PRIVACY POLICY INFERENCE OF USER-UPLOADED IMAGES ON CONTENT SHARING SITESnexgentechnology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
PRIVACY POLICY INFERENCE OF USER-UPLOADED IMAGES ON CONTENT SHARING SITES - I...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
The document proposes an Adaptive Privacy Policy Prediction (A3P) system to automatically generate personalized privacy policies for images uploaded by users to content sharing sites. The system considers several factors that influence privacy preferences, including social context, image content and metadata. It uses a two-level framework where newly uploaded images are first classified into categories, then a policy prediction algorithm generates a recommended policy based on the image category and the user's history. Evaluation shows the system achieves over 90% accuracy in predicting privacy policies.
Privacy policy inference of user uploadednexgentech15
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Detecting Spam Tags Against Collaborative Unfair Through Trust ModellingIOSR Journals
This document discusses methods for detecting spam tags in collaborative tagging systems through trust modeling. It classifies existing approaches into content trust modeling and user trust modeling. Content trust modeling assigns trust scores to content based on tags and users associated with it, while user trust modeling assigns trust scores to users based on their tagging behavior. The document also discusses challenges like evaluating models on multilingual data and lack of publicly available datasets for comparison. It concludes that trust modeling is important for enhancing reliability of social networks and content sharing services.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Image retrieval and re ranking techniques - a surveysipij
There is a huge amount of research work focusing on the searching, retrieval and re-ranking of images in
the image database. The diverse and scattered work in this domain needs to be collected and organized for
easy and quick reference.
Relating to the above context, this paper gives a brief overview of various image retrieval and re-ranking
techniques. Starting with the introduction to existing system the paper proceeds through the core
architecture of image harvesting and retrieval system to the different Re-ranking techniques. These
techniques are discussed in terms of approaches, methodologies and findings and are listed in tabular form
for quick review.
The author is researching the effects of feedback on the motivation of software engineers. In the first stage, the author plans to conduct interviews and diary studies to identify types of feedback received by software engineers and their experiences with feedback. In the second stage, additional studies will explore the impact of feedback on engineers' motivation. Finally, the author will observe engineers at work to see feedback in context and compare to prior findings. The goal is to better understand how feedback affects engineers' motivation and how this is manifested in behavior, productivity, or attitude. Key theories that inform the research are Hackman and Oldman's Job Characteristics Theory and Herzberg's Motivational-Hygiene Theory.
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...inventionjournals
The problem of web search time complexity and accuracy has been visited in many research papers, and the authors discussed many approaches to improve the search performance. Still the approaches does not produce any noticeable improvement and struggles with more time complexity as well. To overcome the issues identified, an efficient multi mode conceptual clustering algorithm has been discussed in this paper, which identifies the similar interested user groups by clustering their search context according to different conceptual queries. Identified user groups are shared with the related conceptual queries and their results to reduce the time complexity. The multi mode conceptual clustering, performs grouping of search queries and users according to number of users and their search pattern. The concept of search is identified by using Natural language processing methods and the web logs produced by the default web search engines. The author designed a dedicated web interface to collect the web log about the user search and the same data has been used to cluster the social groups according to number of conceptual queries. The search results has been shared between the users of identified social groups which reduces the search time complexity and improves the efficiency of web search in better manner
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Bn35364376
1. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 364 | P a g e
User Specific Word Based Image Search from Photo Sharing
Websites
Chelli Sunil Kumar1
, Bandaru A Chakravarthi2
, Y Sowjanya Kumari3
1
PG Student, Department Of CSIT, SACET, Chirala , Prakasam Dist, AP, INDIA.
2
Assistant Professor, Department Of CSE/IT, Mallareddy College of Engg. & Tech., Secunderabad, AP, INDIA.
3
Associate Professor, Department Of CSIT, SACET, Chirala , Prakasam Dist, AP, INDIA.
Abstract
Increasingly developed social sharing websites, like Flickr and Youtube, allow users to create, share, annotate
and comment medias. The large-scale user-generated meta-data not only facilitate users in sharing and
organizing multimedia content, but provide useful information to improve media retrieval and management.
Personalized search serves as one of such examples where the web search experience is improved by generating
the returned list according to the modified user search intents. In this paper, we exploit the social annotations
and propose a novel framework simultaneously considering the user and query relevance to learn to
personalized image search. The basic premise is to embed the user preference and query-related search intent
into user-specific topic spaces. Since the users’ original annotation is too sparse for topic modeling, we need to
enrich users’ annotation pool before user-specific topic spaces construction. The proposed framework contains
two components: 1) A Ranking based Multi-correlation Tensor Factorization model is proposed to perform
annotation prediction, which is considered as users’ potential annotations for the images; 2) We introduce User-
specific Topic Modeling to map the query relevance and user preference into the same user-specific topic space.
For performance evaluation, two resources involved with users’ social activities are employed. Experiments on
a large-scale Flickr dataset demonstrate the effectiveness of the proposed method.
Index Terms—personalized image search, tensor factorization, topic model, social annotation.
I. INTRODUCTION
Keyword-based search has been the most
popular search paradigm in today’s search market.
Despite simplicity and efficiency, the performance of
keyword-based search is far from satisfying.
Investigation has indicated its poor user experience -
on Google search, for 52% of 20,000 queries, searchers
did not find any relevant results [1]. This is due to two
reasons. 1) Queries are in general short and
nonspecific, e.g., the query of “IR” has the
interpretation of both information retrieval and infra-
red. 2) Users may have different intentions for the
same query, e.g., searching for “jaguar” by a car fan
has a completely different meaning from searching by
an animal specialist. One solution to address these
problems is personalized search, where user-specific
information is considered to distinguish the exact
intentions of the user queries and re rank the list
results. Given the large and growing importance of
search engines, personalized search has the
Fig. 1. Toy example for non-personalized (top) and
personalized (bottom) search results for the query
“jaguar”.
search, the rank of a document (web page, image,
video, etc.) in the result list is decided not only by the
query, but by the preference of user. Fig. 1 shows a toy
example for non-personalized and personalized image
search results. The non-personalized search returned
results only based on the query relevance and displays
jaguar car images as well as wild cat on the top. While
personalized search consider both query relevance and
user preference, therefore the personalized results from
an animal lover rank the leopard images on the top.
This provides a natural two-step solution scheme. Most
of the existing work follow this scheme and
decompose personalized search into two steps:
RESEARCH ARTICLE OPEN ACCESS
2. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 365 | P a g e
computing the non personalized relevance score
between the query and the document, and computing
the personalized score by estimating the user’s
preference over the document. After that, a merge
operation is conducted to generate a final ranked list.
While this two-step scheme is extensively utilized, it
suffers from two problems. 1) The interpretation is less
straight and not so convinced. The intuition of
personalized search is to rank the returned documents
by estimating the user’s preference over documents
under certain queries. Instead of directly analyzing the
user-query-document correlation, the existing scheme
approximates it by separately computing a query-
document relevance score and a user-document
relevance score. 2) How to determine the merge
strategy is not trivial.1 In this paper, we
simultaneously considers the user and query
dependence and present a novel framework to tackle
the personalized image search problem.
To investigate on user preference and perform user
modeling, the popular social activity of tagging is
considered. Collaborative tagging has become an
increasingly popular means for sharing and organizing
resources, leading to a huge amount of user-generated
annotations. Online photo sharing websites, such as
Flickr,
Fig. 2. The proposed framework.
Pinterest allow users as owners, taggers, or
commenters for their contributed contents to interact
and collaborate with each other in a social media
dialogue. Various researchers have investigated the
applicability of social annotations to improve web
search. Recently, social annotations are employed for
automatic evaluation of personalized search A
fundamental assumption is that, the users’ tagging
actions reflect their personal relevance judgement.
For example, if a user tagged “festival” to an image,
it is probable that the user will consider this image as
relevant if he/she issues “festival” as a query.
Illustrated by this, the intuition of this paper is that if
the users’ annotations to the images are available, we
can directly estimate the users’ preference under
certain queries. The fact is that the original
annotations available is not enough for user
preference mining. Therefore, we transfer the
problem of personalized image search to users’
annotation prediction. Moreover, as queries and tags
do not follow simple one-to-one relationship, we
build user-specific topic spaces to exploit the
relations between queries and tags.
A. Framework
The framework of this paper is shown in
Fig.2. It contains two stages: offline model training
stage and online personalized search response stage.
For the offline stage, three types of data
including users, images and tags as well as their
ternary interrelations and intra-relations are first
collected.3 We then perform users’ annotation
prediction. Many methods for tag recommendation
and prediction have been proposed in social
bookmark sites, e.g., Bibsonomy, Del.icio.us,
Last.fm, etc. Since the photo sharing websites utilize
a different tagging mechanism that repetitive tags are
not allowed for unique images, besides the common
noisy problem, it has more severe sparsity problem
than other social tagging systems. To alleviate the
sparsity and noisy problem, we present a novel
method named Ranking based Multi correlation
Tensor Factorization (RMTF) to better leverage the
observed tagging data for users’ annotation
prediction. Zhu et. al.has demonstrated that the
semantic space spanned by image tags can be
approximated by a smaller subset of salient words
from the original space. Illustrated by this, we
employ low rank approximation to extract the
compact representation for image, tag and user, and
at the same time reconstruct the user-image-tag
3. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 366 | P a g e
ternary relations for annotation prediction. With the
observed user-tag-image ternary relations as input,
the reconstructed ternary relations can be viewed as
users’ potential annotations for the images.
Following the assumption we mentioned in
the introduction, we can straightly utilize the
predicted user annotations for personalized image
search, i.e., if a user has a high probability to assign
the tag t to an image, the image should be ranked
higher when the user issues query t. However, this
formulation has two problems. 1) It is unreasonable
to assign the query to a single tag in the tag
vocabulary, e.g., when a user searches “cheerdance”,
he/she would like the images that he/she annotated
with semantic related tag “cheerleader” are also
ranked higher. 2) There are variations in individual
user’s tagging patterns and vocabularies, e.g., the tag
“jaguar” from an animal specialist should be related
to “leopard”, while a car fan will consider “jaguar”
more related to “autos”. To address the two
problems, we perform User-specific Topic Modeling
to build the semantic topics for each user. The user’s
annotation for an image is viewed as document. The
individual tag to the image is word. User’s
annotations for all the images constitute the corpus.
As the original annotation is too sparse for topic
modeling, we use the reconstructed ternary relations
as the document collections. The user’s topic
distribution per image can be considered as his/her
preference over the image on the learned user-
specific topic space. Therefore, after the offline stage,
two outcomes are stored in the system, the user-
specific topics and topic-sensitive user preferences.
For the online stage, when a user u submits a
query q, we first map the query q to user u-specific
topics. The query distribution is then sent to the rank
module and employed as the weight on topics to
calculate the user u’s topic sensitive preferences over
the images. Finally, the images are ranked according
to the calculated user’s preferences, which
simultaneously considers the query and user
information.
The contributions of this paper are
summarized as three folds:
• We propose a novel personalized image search
framework by simultaneously considering user
and query information. The user’s preferences
over images under certain query are estimated by
how probable he/she assigns the query-related
tags to the images.
• A ranking based tensor factorization model
named RMTF is proposed to predict users’
annotations to the images.
• To better represent the query-tag relationship, we
build user-specific topics and map the queries as
well as the users’ preferences onto the learned
topic spaces.
II. RELATED WORK
In recent years, extensive efforts have been
focusing on personalized search. Regarding the
resources they leveraged, explicit user
profile,relevance feedback, user history data
(browsing log , click-through data and social
annotations etc.), context information (time,
location, etc.) and social network are exploited. For
the implementation there are two primary strategies,
query refinement and result processing. In the
following we review the related work by the strategy
they used.
Query Refinement, also called Query
Expansion, refers to the modification to the original
query according to the user information. It includes
augmenting the query by other terms and changing
the original weight of each query term. Kraft et al.
utilized the search context information collected from
users’ explicit feedback to enrich the query terms.
Chirita et al. proposed five generic techniques for
providing expansion terms, ranging from term and
expression level analysis up to global co occurrence
statistics and external thesauri. While, Teevan et al.
reassigned the weights of original query terms using
BM25 weighting scheme to incorporate user interests
as collected by their desktop indexes. We do not
explicitly perform query refinement in this paper.
However, mapping the queries into user-specific
topic spaces can be considered as implicit query
refinement.
Result Processing can be further classified
into result filtering and re-ranking. Result filtering
aims to filter irrelevant results that are not of interest
to a particular user. While, result re-ranking focuses
on re-ordering the results by the degree of users’
preferences estimated. Since our work falls into this
category, we mainly review the related work on result
re-ranking. Chirita et al. conducted an early work by
reranking the search results according to the cosine
distance between each URL and user interest profiles
constructed. Qiu et al. extended Topic-Sensitive
PageRank by incorporating users’ preference vectors.
By aggregating the search results from multiple
search engines, Liu et al. introduced a new method
for visual search reranking called CrowdReranking.
A typical work is performed by Xu et al. in which the
overall ranking score is not only based on term
similarity matching between the query and the
documents but also topic similarity matching
between the user’s interests and the documents’
topics. In the similar spirits, Cai formalized query
and user relevance measurement separately as fuzzy
requirement satisfaction problem. Lu et al. utilized a
co-clustering method to extract latent interest
dimensions, and re-rank the images by combining
latent interest based user preference and query
relevance. In our work, there is also a topic space to
model user preference. However, regarding the
variations in user’s tagging vocabularies, we build
4. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 367 | P a g e
user-specific topics and calculate topic-sensitive user
preference over images. Most of the existing work
decompose the overall ranking score into query
relevance and user preference and generate two
separated ranked list. While in this paper, we map the
queries into the same user-specific topic space and
directly compute the users’ preference under certain
queries.
III. RANKING BASED MULTI-
CORRELATION TENSOR
FACTORIZATION
In this section, we present the algorithm for
annotation prediction. Table I lists the key notations
used in this paper. There are three types of entities in
the photo sharing websites. The tagging data can be
viewed as a set of triplets. Let denote the
sets of users, images, tags and the set of observed
tagging data is denoted by , i.e.,
each triplet
means that user u has annotated
image i with tag t. The ternary interrelations can then
constitute a three dimensional tensor
which is defined as:
Fig.6(a) shows the tensor constructed from the
running example in Fig.2.
Predicting the users’ annotations to the
images are related to reconstructing the user-tag-
image ternary interrelations. We use Tucker
decomposition [31], a
general tensor factorization model, to perform the
low-rank approximation. In Tucker decomposition,
the tagging data Y are estimated by three low rank
matrices and one core tensor:
where ×n is the tensor product of multiplying a
matrix
on mode n. Each matrix corresponds to one factor.
The core tensor contains the
interactions between the different factors. The ranks
of decomposed factors are denoted by rU; rI ; rT and
this is called rank-(rU; rI ; rT ) Tucker
decomposition. Under Tucker decomposition, we
need to design appropriate objective function to
optimize the latent factors U; I; T;C and then
calculate the reconstructed tensor by Eq.2.
In this paper, a model named RMTF is
proposed to design the objective function. To better
leverage the observed tagging data, we first introduce
a novel ranking based optimization scheme for
representation of the tagging data. Then the multiple
intra-relations among users, images and tags are
utilized as the smoothness constraints to tackle the
sparsity problem.
A. Ranking based Optimization Scheme
A direct way to approximate Y is to
minimize the sum of
point-wise loss on
where . As this
optimization scheme tries to fit to the numerical
values of 1 and 0, we refer it as the 0/1 scheme.
However, under the situation of social image tagging
data, the semantics of encoding all the unobserved
data as 0 are incorrect, which is illustrated with the
running example:
• Firstly, the fact that user3 has not given any tag
to image2 and image4 does not mean user3
considering all the tags are bad for describing the
images. Maybe he/she does not want to tag the
image or has no chance to see the image.5
• Secondly, user1 annotates image1 with only
tag3. It is also unreasonable to assume that other
tags should not be annotated to the image, as
some concepts may be missing in the user-
generated tags and individual user may not be
familiar to all the relevant tags in the large tag
vocabulary.
According to the optimization function in
Eq.3, 0/1 scheme tries to predict 0 for both cases. To
address the above two issues, in this paper, we
present a ranking
5. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 368 | P a g e
Fig. 3. Tagging data interpretation. (a) 0/1 scheme (b)
ranking scheme optimization scheme which
intuitively takes the user tagging behaviors into
consideration.
Firstly, we note that only the qualitative
difference is important and fitting to the numerical
values of 1 and 0 is unnecessary. Therefore, instead
of solving an point-wise classification task, we
formulate it as a ranking problem which uses tag
pairs within each user-image combination (u; i) as
the training data and optimizes for correct ranking.
We provide some notations for easy
explanation. Each user image combination (u; i) is
defined as a post. The set of observed posts is
denoted as
Note that the ranking optimization is performed over
each post and within each post (u; i) a positive tag set
and a negative tag set are desired to
construct the training pairs. We assume that any tag
is a better description for image i than all
the tags . The pairwise ranking
relationships can be denoted as:
The optimization criterion is to minimize the violation of the
pairwise ranking relationships in the reconstructed tensor
which leads to the following objective:
where is a monotonic increasing
function (e.g., the logistic sigmoid function or
Heaviside function).
Secondly, for the training pair
determination. The neutral triplets constitute a set
:
It is arbitrary to treat the neutral triplets as either
positive or negative and we remove all the triplets in
M from the learning process (filled by bold question
marks in Fig.6(b)).
We then consider two characteristics of the
user tagging behaviors to choose and .
On one hand, some concepts may be missing in the
user-generated tags. We assume that the tags co-
occurring frequently are likely to appear in the same
image (we call it context-relevant). On the other
hand, users will not bother to use all the relevant tags
to describe the image. The tags semantic-relevant
with the observed tags are also the potential good
descriptions for the image. To perform the idea, we
build a tag affinity graph based on tag semantic
and context intra-relations (detailed in Section III.B).
The tags with the k-highest affinity values are
considered semantic-relevant and context-relevant.
Given a post , the observed tags
constitute a positive tag set (the corresponding
triplets are filled by plus signs in Fig.6(b)):
Instead of adding semantic and context-relevant tags
into the positive set , we only keep the
unobserved tags semantic irrelevant and context-
irrelevant to any of the observed tags to
form the negative tag set:
where means the set of tags relevant to the
annotated tags in post (u; i). Then
when tag1 and tag2 are relevant to
tag3. The minus signs indicate the filtered negative
triplets in Fig.6(b). The triplets corresponding to tags
are also removed from the learning
process and filled by plain question marks.
B. Multi-correlation Smoothness Constraints
Photo sharing websites differentiate from
other social tagging systems by its characteristic of
self-tagging: most images are only tagged by their
owners. Fig.4(a) shows the #tagger statistics for
Flickr and the webpage tagging system Del.icio.us.
We can see that in Flickr, 90% images have no more
than 4 taggers and the average number of tagger for
each image is about 1.9. However, the average tagger
for each webpage in Del.icio.us is 6.1. The severe
6. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 369 | P a g e
sparsity problem calls for external resources to enable
information propagation.
In addition to the ternary interrelations, we also
collect multiple intra-relations among users, images
and tags. These intra-relations constitute the affinity
graphs
respectively. We assume that two items with high
affinities should be mapped close to each other in the
learnt factor subspaces. In the following, we first
introduce how to construct the tag affinity graph,6
and then incorporate them into the tensor
factorization framework. Tag affinity graph .
To serve the ranking based optimization scheme, we
build the tag affinity graph based on the tag semantic
relevance and context relevance. The context
relevance of tag tm and tn is simply encoded by their
weighted co-occurrence in the image collection:
For tag semantic relevance, we estimate the semantic
relevance between tag and based on their
WordNet distance:
where IC(·) is the information content of tag, and
is their least common subsumer in the
WordNet taxonomy. The tag affinity graph is
constructed as:
where and are the weights
of context relevance and semantic relevance (in the
experiment, we choose and .
The affinity graphs can be utilized as the
regularization terms to impose smoothness
constraints for the latent factors. Take the image
affinity graph WI and the image factor matrix I as
example, the regularization term is:
where denotes the Frobenius norm. The basic
ideaisto make the latent representations of two
images as close as possible if there exists strong
affinity between them. Wecan achieve this by
minimizing the trace of isthe Laplacian
matrix for the image affinity matrix . We can
build similar regularization terms for the user and tag
factors. Combining with Eq.6, we obtain the
following overall objective function:
where is l-1
regularization term to penalize large parameters, _
and _ are weights controlling the strength of
corresponding constraints. Obviously, directly
optimizing Eq.14 is infeasible and we employ an
iterative optimization strategy.
IV. USER-SPECIFIC TOPIC
MODELING
With the reconstructed user-tag-image
ternary interrelations, we can directly perform the
personalized image search: when user u submits a
query q, the rank of image i is inversely proportional
to the probability of u annotating i with tag q:
However in practice, the queries and tags do
not follow one to- one relationship - one query
usually corresponds to several related tags in the tag
vocabulary. Besides, the query-tag correspondence
differs from user to user. Therefore, we build topic
spaces for each user to exploit this user-specific one-
to many relationship.We investigate on a Flickr
dataset of 270K images that the average number of
annotated images per user is only 30. The detailed
distribution is shown in Fig.4(b). Obviously the
original annotation is too sparse to perform topic
modeling, hence we employ the predicted
annotations.7 Particularly,for each user u, the tags
with100 highest are reserved as the
annotations for image i. Each user’s annotations to all
the images constitute one corpus, and we choose
Latent Dirichlet Allocation (LDA, [32]) to perform
topic modeling. The individual tag is viewed as word,
while the user’s annotation to one image corresponds
to one document.
LDA assumes that in one corpus, documents
are generated from a set of K latent topics {topic1; · ·
· ; topicK}. Document ti is the tags assigned to image
i by individual user. In ti, each word t is associated
with a latent topic. The generative process for user
u’s annotation corpus Du is : For each document ti in
a corpus Du,
• Sample a K-vector document-topic distribution
from a Dirichlet distribution;
• For each word t, sample topic assignment j
according to and draw a word from the j-th topic-
word distribution The generative
model is fitted using a Gibbs sampler.
7. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 370 | P a g e
After the user-specific topic modeling, for
each user u, we obtain 1) User-specific topics
and 2) Topic sensitive user
preference . In
Table II, we list the first three dominating topics for
two example users. Each topic is characterized by its
eight most probable tags. The rank of the user u-
specific topics is decided
by the probability that user u is
interested in topic j. This can be calculated by
aggregating user u’s preference over all the images:
From the user-specific topics, we can see:
• User’s interest profile, e.g., user A is likely to be a
military fan who also likes digital product and sports,
while user B is keen at religion and interests in
gardening and aerocraft;
• The same tag may have different topic posterior
distributions for different users, e.g., for user A,
“aircraft” occurs frequently in a military-related
topic, while for user B, “aircraft” returns to its literal
sense of air vehicle.
A. Online Personalized Search
In the online stage, when user u submits a
query q, we first perform user-specific query
mapping - estimate the conditional probability that q
belongs to user u-specific topics:
From Table II, since user A has a principle interest on
topic and “aircraft” has a high
probability in topic 1 (p(q|topic1; u)), when user A
searches “aircraft”, the query will have a high
proportion
on user A’s topic 1. The query distribution is then
utilized as weights to compute user u’s topic-
sensitive preferences over the images under the query
q. The rank of image i can be obtained as:
When user A searches “aircraft”, the images
likely to be annotated by military-related tags are
ranked higher according to Eq.18. While, when user
B searches “aircraft”, the images likely to be
annotated by aerocraft-related tags will be ranked
higher. We can see that the query relevance and user
preference are simultaneously integrated into this
personalized formulation.
V. EXPERIMENTS
A. Dataset
We perform the experiments on a large-scale
web image dataset, NUS-WIDE [33]. It contains
269,648 images with 5,018 unique tags collected
from Flickr. We crawled the images’ owner
information and obtained owner user ID of 247,849
images.10. The collected images belong to 50,120
unique users. Fig.4 shows the distributions of #tagger
and #tagged images We investigate on the sparseness
of several social tagging systems in Table III, where
sparseness is defined as:
The results presented are not meant to be exhaustive
but illustrative of the fact that Flickr has a more
severe sparseness problem. We select the users
owning no less than 15 images and keep their images
to perform the tensor factorization, which is referred
as NUS-WIDE15.
B. Parameter Setting
NUS-WIDE15 is randomly split into two
parts, 90% for training and testing (denoted as S), and
10% for validation (denoted as V). The result of
annotation prediction directly affect the performance
of personalized search. In our work, we select
parameters according to the performance of
annotation prediction.There are three sets of
parameters for the proposed RMTF+LDA model. The
first three parameters are the rank of factor matrices,
8. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 371 | P a g e
rU, rI , rT . we simply choose the ranks proportional
to the original dimensions and set rU =
50; rI = 250; rT = 5. This guarantee that the same
ratio of energies for different modes are preserved.
The second set of parameters are the regularization
weights They control how much the tensor
decomposition incorporates the information of
affinity intrarelations. By keeping rU = 50; rI = 250;
rT = 5, we conduct a simple training of RMTF to
choose and on the validation set. For each user,
one post is randomly removed for annotation
prediction evaluation. Fig. 5 illustrates the impacts of
and on the F1 score of annotation prediction for
top-10 recommended tags. We can see that the
performance remain relatively
Fig. 4. (a) The cumulative distribution of document ratio w.r.t. the number of taggers for Flickr and Del.icio.us;
(b) The number of tagged images per user for Flickr
steady when and change within a certain range.
We set = 0:01 and = 0:001, which achieves the
highest average F1 score. The most important
parameter for user specific topic modeling is the
number of latent topics for each user. For now the
number is set same for different users and K = 20.
We investigate the influence of K in the following
experiment.
C. Annotation Prediction
We propose the novel RMTF model for
users’ annotation prediction. In this subsection, we
first evaluate the performance of RMTF for
annotation prediction. Following the evaluation
process from [13], for each user we randomly remove
all triplets he/she has annotated for one image to
constitute the test set – i.e., we remove one
post for each user. The remaining observed user-
image-tag triplets are used for regularized tensor
factorization. Then we learn the model and predict
top-N lists for each of the removed posts
based on the reconstructed tensor from Eq.2. We
compute the recall and precision of the top-N
recommended tags and report the F1 score of the
average recall and precision:
9. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 372 | P a g e
Fig. 6. The F1 score of annotation prediction for
different methods.
Four annotation prediction methods are
performed for comparisons: most popular tags for
image (Popular I), most popular tags for user
(Popular U), FolkRank and HOSVD . Fig. 6
illustrates the results. It is shown that RMTF
generally performs the best, and with the increasing
number of recommended tags, the F1 score decreases
less steeper for RMTF than the other methods. This
coincides with our discussions in the introduction that
the proposed ranking scheme as well as exploiting
the tag semantic-and-context relevance better
alleviates the severe sparsity and noisy problem for
Flickr dataset.
D. Personalized Search
In the research community of personalized
search, evaluation is not an easy task since relevance
judgement can only be evaluated by the searchers
themselves. The most widely accepted approach is
user study where participants are asked to judge the
search results. Obviously this approach is very costly.
In addition, a common problem for user study is that
the results are likely to be biased as the participants
know that they are being tested. Another extensively
used approach is by user query logs or click through
history. However, this needs a large-scale real search
logs, which is not available for most of the
researchers.
Social sharing websites provide rich
resources that can be exploited for personalized
search evaluation. User’s social activities, such as
rating, tagging and commenting, indicate the user’s
interest and preference in a specific document.
Recently, two types of such user feedback are utilized
for personalized search evaluation. The first approach
is to use social annotations .The main assumption
behind is that the documents tagged by user u with
tag t will be considered relevant for the personalized
query (u; t). Another evaluation approach is proposed
for personalized image search on Flickr ,where the
images marked Favorite by the user u are treated as
relevant when u issues queries. The two evaluation
approaches have their pros and cons and supplement
for each other. We use both in our experiments and
list the results in the following. We select two state-
of-the-art models as the baseline
• Topic-based: topic-based personalized search using
folksonomy.
• Preference-based: personalized image search by
predicting user interests-based preference .
Note that both methods follow the two-step
scheme: the overall ranking is decided by separately
computing query relevance and user preference. In
addition, we also compared the performances of the
proposed model with different settings:
• TF 0/1 LDA: TF without smoothness constraints,
optimization under the 0/1 scheme, using user-
specific topic modeling;
• MTF 0/1 LDA: TF with multi-correlation
smoothness
constraints, optimization under the 0/1 scheme, using
userspecific topic modeling;
• RMTF LDA, the proposed model: annotations
predictions by RMTF, using user-specific topic
modeling;
• RMTF: Directly using the RMTF-based predicted
annotations for personalized rank according to Eq.15.
1) Annotation-based Evaluation: We follow Xu’s
[2] evaluation framework and first compare the
performances according to users’ original
annotations. To perform the evaluation in the
situations of users with different amount of original
annotations, we build two test scenarios: 1) 30
randomly selected users who tagged 10-30 images
and their tagging records, denoted as NUS-WIDE15
A10 30. 2) 30 randomly selected users who tagged
more than 100 images and their tagging records,
denoted as NUS-WIDE15 A100. For NUSWIDE15
A100, the overlapping 18 tags the 50 users used are
selected as the test queries, while for NUS-WIDE15
A10 30, the number of test queries is 11. The
statistics of the testing sets are shown in Table IV. In
order to reduce the dependency between original
annotations and evaluation, we remove the tagging
data related to the test queries. It is done as follows:
for each personal query (u; t), we remove the triplets
from the training set.
In the experiments, we use Average Precision
(AP) as the evaluation metric, which is a widely used
relevance metric evaluating the performance of the
top documents in the ranked list. AP is defined as:
where i is the position (i.e., rank) of the document, n+
is the number of the relevant documents and r(i)
denotes the relevance of the document in position i.
In our case, a binary value for r(i) is used by setting it
to 1 if the document is relevant and 0 otherwise. The
Mean Average Precision (MAP) is the mean of the
10. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 373 | P a g e
APs of all queries for one user, and the mean of the
MAPs of all test users is referred as mMAP:
Here AP(i; j) represents the AP value of the
query
Fig. 7. The mMAP value of personalized search for different methods (a) Annotation-based; (b) Favorite-based
for the user, and Nu;Nq is the number of test
users and queries respectively.
The results are shown in Fig.7(a). Non-
personalized denotes the non-personalized rank
result by only considering the query relevance. We
can see that all the personalized methods outperform
the non-personalized scheme. Comparing between
the two test scenarios of NUS-WIDE15 A10 30 and
NUSWIDE15 A100, the performances of
personalized methods improve as the test users’
original annotations increase. This is reasonable as
these methods utilize the social annotation resources
and the more user feedback is available, the more
accurate user preferences can be estimated. What is
interesting is that the preference-based model and the
proposed model are more sensitive to the amount of
original annotations. The reason may be that and our
methods extract topic spaces by explicitly exploiting
the tagging data, while in the topic based model ,the
topic space is pre-defined and the original annotation
is just used to generate the topic vector.
Focusing on either test scenario, the
performance of the proposed RMTF LDA, even MTF
0/1 LDA, is superior than the baseline methods,
which demonstrate the advantage of simultaneously
considering query relevance and user preference over
the separate schemes. Depending on one-to-one
query tag assumption, the performance of RMTF
deteriorates dramatically without the user-specific
topic modeling. Moreover, RMTF LDA outperforms
MTF 0/1 LDA, showing the advantage of the
proposed ranking scheme over the conventional 0/1
scheme. Without smoothness priors, TF 0/1 fails to
preserve the affinity structures and achieves inferior
results.
2) Favorite-based Evaluation: There is a delicate
issue with annotation-based evaluation. Both the
input to the personalized models and the evaluation
for the output results are based on the original
annotations. Although the specific tagging data (u; :;
t) have been removed when testing the personal
query (u; t), as individual user’s tagging vocabulary
tends to be limited, the remaining annotations will
implicitly provide the association between u and t.
For example, assuming one user u usually tag
“wildlife” and “animal” together, when he/she issues
“wildlife” as test query, though all (u; :;wildlife) have
been removed from the training process, regarding
“wildlife” and “animal” are likely to have a close
relation in the user specific topics, the images tagged
by “animal” will be given high probability and guide
the final rank. On Flickr, users are encouraged to
express their preference on images by adding
Favorite marks. Illustrated by Lu’s evaluation
framework , we employ users’ Favorite marks for
evaluation, which are not used in the training process.
11. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 374 | P a g e
This guarantees that personalization is evaluated
without any prior knowledge.
To be consistent with the annotation-based
evaluation scheme, we also build two test scenarios
for the favorite based evaluation: 1) 30 randomly
selected users who added Favorite to 10-30 images,
denoted as NUS-WIDE15 F10 30. 2) all the 19 users
who added Favorite to more 100 images, denoted as
NUS-WIDE15 F100. 15 tags frequently appearing in
the annotation of those favorite images are selected
as the test queries. The metric of mMAP is utilized to
evaluate the performance and the results are
demonstrated in Fig.7(b). We have the following
observations:
• The mMAP is relatively low compared with
Annotation based evaluation. This phenomenon
reflects the problem of Favorite-based evaluation
scheme: the Favorite images are considered relevant
for all the test queries. As no query information is
involved, for those queries non-relevant with the
topic of the Favorite images, the AP tends to be low;
• Comparing between the two test scenarios, the
average
performance of NUS-WIDE15 F100 also improves
over NUS-WIDE15 F10 30, but not as significant as
in Annotation-based evaluation. One possible reason
for the improvement is that those users having more
Favorite marks are active users who are likely to also
attend more interest groups and tag more images.
While, the improvement is not so significant
demonstrates that the Favorite-based evaluation
scheme is less sensitive to the amount of original
annotations;
• Another obvious difference from the results of
Annotation based evaluation is that the performance
of TF 0/1 and MTF 0/1 LDA degrade dramatically.
The mMAP of TF 0/1 is even lower than the non
personalized method. For the Annotation-based
evaluation, TF 0/1 achieves comparable results due to
the implicit prior knowledge provided by the original
annotations. By utilizing the Favorite marks, a
heterogenous resource for evaluation, the implicit
prior is eliminated. Fig. 8 displays exemplary search
results for the query “aircraft”. The top six non-
personalized results and the personalized results of
User A and User B mentioned in Section IV.A are
shown. We can see that by simultaneously
considering query relevance and user information, the
proposed RMTF LDA captures the user’s preference
under certain topics. As a result of mapping “aircraft”
to Topic 1 of Table II, the top search results for user
A mainly focus on aerocrafts. While, for user B, the
top search results are basically military related, which
coincides with user B’s preference. For the baseline
method which separate query relevance and user
preference, sometimes the search results are hard to
interpret. For example, the second and third images
for user B in Fig. 8(a) are ranked higher because user
B has a major interest in religion and flower.
However, these images have little relation with
aircraft. We note that for some general queries which
have clear search intents, personalized search tends to
fail. Fig. 9 illustrates one of
Fig. 8. Example of non-personalized (top) and personalized (middle for User A and bottom for User B) search
results for query “aircraft” (a) Topic-based method; (b) RMTF LDA
Fig. 9. Example of non-personalized (top) and personalized (middle for User A and bottom for User B) search
results for query “beach” (a) Topic-based method; (b) RMTF LDA
such examples. With “beach” having common
understanding to different users, incorporating user
information will generate confusing search results.
There are literatures discussing the issue about when
12. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 375 | P a g e
to perform personalization. It seems that the benefit
of personalization is highly dependent on the
ambiguity of the query. Since there is no conclusion
to this problem, in this paper we focus on the
problem of how to perform personalization and
discussion of when to perform personalization is
beyond the scope of this paper.
Fig. 10. The influence of topic number K (a) mMAP for all test users; (b) MAP for two users from NUS-
WIDE15 A100
3) Influence of Topic Number K: For the standard
LDA, the number of latent topics needs to be
specified. In the above experiments, we set the same
number of topics for all users and K = 20. In the
following, we variate the selection of K and
investigate the influence of topic number. We utilize
the annotation-based evaluation scheme to compute
mMAP. The results are illustrated in Fig.10. It is
shown that mMAP displays no definite trend as K
changes (Fig.10(a)), while for individual user C and
user D there exist obvious optimal K (See Fig.10(b),
obviously user C and D have an optimal K = 10 and
K = 25 respectively). This observation is in line with
the expectations that users have different topic spaces
and validate the necessity of user-specific topic
modeling. In addition, user-specific topic number
should be specified in the future work. There is a
number of extension work on standard LDA to
automatically select the number of topics. The most
common one is HDP-LDA, which uses Hierarchical
Dirichlet Processes (HDP, [37]) to model the
Dirichlet mixtures in LDA nonparametrically.
V. CONCLUSION AND FUTURE
WORK
How to effectively utilize the rich user
metadata in the social sharing websites for
personalized search is challenging as well as
significant. In this paper we propose a novel
framework to exploit the users’ social activities for
personalized image search, such as annotations and
the participation of interest groups. The query
relevance and user preference are simultaneously
integrated into the final rank list. Experiments on a
large-scale Flickr dataset show that the proposed
framework greatly outperforms the baseline.
In the future, we will improve our current
work along four directions. 1) In this paper, we only
consider the simple case of one word-based queries.
Actually, the construction of topic space provides a
possible solution to handle the complex multiple
words-based queries. We will leave it for our future
work. 2) During the user-specific topic modeling
process, the obtained user-specific topics represent
the user’s distribution on the topic space and can be
considered as user’s interest profile. Therefore, this
framework can be extended to any applications based
on interest profiles. 3) For batch of new data (new
users or new images), we directly restart the RMTF
and user-specific topic modeling process. While, for
a small amount of new data, designing the
appropriate update rule is another future direction. 4)
Utilizing large tensors brings challenges to the
computation cost. We plan to turn to parallelization
(e.g. parallel MATLAB) to speed up the RMTF
converge process. Moreover, the distributed storing
mechanism of parallelization will provide a
convenient way to store very large matrices and
further reduce the storage cost.
REFERENCES
[1] B. Smyth, “A community-based approach to
personalizing web search,” Computer,
vol. 40, no. 8, pp. 42–50, 2007.
[2] S. Xu, S. Bao, B. Fei, Z. Su, and Y. Yu,
“Exploring folksonomy for personalized
search,” in SIGIR, 2008, pp. 155–162.
[3] Koifman, N. Har’El, I. Ronen, E. Uziel, S.
Yogev, and S. Chernov, “Personalized social
search based on the user’s social network,”
in CIKM, 2009, pp. 1227–1236.
[4] Y. Cai and Q. Li, “Personalized search by
tag-based user profile and resource profile in
collaborative tagging systems,” in CIKM,
2010, pp. 969–978.
13. Chelli Sunil Kumar et al. Int. Journal of Engineering Research and Application www.ijera.com
Vol. 3, Issue 5, Sep-Oct 2013, pp.364-376
www.ijera.com 376 | P a g e
[5] D. Lu and Q. Li, “Personalized search on
flickr based on searcher’s preference
prediction,” in WWW (Companion Volume),
2011, pp. 81– 82.
[6] P. Heymann, G. Koutrika, and H. Garcia-
Molina, “Can social bookmarking improve
web search?” in WSDM, 2008, pp. 195–206.
[7] S. Bao, G.-R. Xue, X. Wu, Y. Yu, B. Fei,
and Z. Su, “Optimizing web search using
social annotations,” in WWW, 2007, pp.
501–510.
[8] D. Zhou, J. Bian, S. Zheng, H. Zha, and C.
L. Giles, “Exploring social annotations for
information retrieval,” in WWW, 2008, pp.
715–724.
[9] J. Tang, S. Yan, R. Hong, G. Qi and T.
Chua, “Inferring Semantic Concepts from
Community-Contributed Images and Noisy
Tags,” in ACM Multimedia, 2009, pp. 223–
232.
[10] J. Tang, H. Li, G. Qi and T. Chua, “ Image
Annotation by Graph-Based Inference With
Integrated Multiple/Single Instance
Representations,” in IEEE Trans.
Multimedia, 2010, vol. 12, no. 2, pp. 131–
141, 2010.
[11] M. J. Carman, M. Baillie, and F. Crestani,
“Tag data and personalized information
retrieval,” in SSM, 2008, pp. 27–34.
[12] R. J¨aschke, L. B. Marinho, A. Hotho, L.
Schmidt-Thieme, and G. Stumme, “Tag
recommendations in folksonomies,” in
PKDD, 2007, pp. 506–514.
[13] R. J¨aschke, L. B. Marinho, A. Hotho, L.
Schmidt-Thieme, and G. Stumme, “Tag
recommendations in social bookmarking
systems,” AI Commun., vol. 21, no. 4, pp.
231–247, 2008.
[14] P. Symeonidis, A. Nanopoulos and Y.
Manolopoulos, “A Unified Framework for
Providing Recommendations in Social
Tagging Systems Based on Ternary
Semantic Analysis,” IEEE Trans. Knowl.
Data Eng., vol. 22, no. 2, pp. 179–192,
2010.
[15] G. Zhu, S. Yan, and Y. Ma, “Image Tag
Refinement Towards Low-Rank, Content-
Tag Prior and Error Sparsity,” in ACM
Multimedia, 2010, pp. 461–470.
[16] J. Teevan, M. R. Morris, and S. Bush,
“Discovering and using groups to improve
personalized search,” in WSDM, 2009, pp.
15–24.
[17] P.-A. Chirita, W. Nejdl, R. Paiu, and C.
Kohlsch¨utter, “Using odp metadata to
personalize search,” in SIGIR, 2005, pp.
178–185.
[18] R. Kraft, F. Maghoul, and C.-C. Chang,
“Y!q: contextual search at the point of
inspiration,” in CIKM, 2005, pp. 816–823.
[19] K. Sugiyama, K. Hatano, and M.
Yoshikawa, “Adaptive web search based on
user profile constructed without any effort
from users,” in WWW, 2004, pp. 675–684.
[20] J.-T. Sun, H.-J. Zeng, H. Liu, Y. Lu, and Z.
Chen, “Cubesvd: a novel approach to
personalized web search,” in WWW, 2005,
pp. 382–390.
[21] F. Qiu and J. Cho, “Automatic identification
of user interest for personalized search,” in
WWW, 2006, pp. 727–736.
[22] Y. Liu, T. Mei and X. Hua,
“CrowdReranking: exploring multiple
search engines for visual search reranking,”
in SIGIR, 2009, pp. 500–507.
[23] N. D. Lane, D. Lymberopoulos, F. Zhao,
and A. T. Campbell, “Hapori: context-based
local search for mobile phones using
community behavioral modeling and
similarity,” in UbiComp, 2010, pp. 109–118.
[24] J. E. Pitkow, H. Sch¨utze, T. A. Cass, R.
Cooley, D. Turnbull, A. Edmonds, E. Adar,
and T. M. Breuel, “Personalized search,”
Commun. ACM, vol. 45, no. 9, pp. 50–55,
2002.
[25] P.-A. Chirita, C. S. Firan, and W. Nejdl,
“Personalized query expansion for the web,”
in SIGIR, 2007, pp. 7–14.
[26] J. Teevan, S. T. Dumais, and E. Horvitz,
“Personalizing search via automated
analysis of interests and activities,” in
SIGIR, 2005, pp. 449– 456.
Y.Sowjanya kumari received her B.tech
degree from NBKRIST, vidya nagar,
India in 2002. She received her M.tech
degree from JNTU Kakinada in 2004.
She is currently pursuing the Ph.D in
JNTU Kakinada in the area of image processing.
Bandaru A Chakravarthi received his
B B .Tech degree from SACET, Chirala, India in r
received his M.Tech degree from JNTU a Kakinada
in 2012. He is currently working as A
Asst. Prof. in Mallareddy Institute of Engg. & T
Tech., Secunderabad, India.
Chelli Sunil kumar received B.tech
degree from chirala engineering college
which is affiliated to JNTU Kakinada.
Currently he is pursuing M.tech in
St.Ann’s college of engineering and technology
which is affiliated to JNTU Kakinada.