The document presents a method for building temporal latent topic user profiles to improve search personalization. It applies latent Dirichlet allocation to clicked documents over different time scales to build long-term, daily, and session-based user profiles. These temporal profiles are used to re-rank search results based on their similarity to the profiles. An evaluation on query log data demonstrates the temporal profiles significantly outperform baselines and improve search performance metrics compared to non-temporal profiles. The session-based profile performs best, followed by the daily profile.
Context Sensitive Search String Composition Algorithm using User Intention to...IJECEIAES
Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.
QUERY SENSITIVE COMPARATIVE SUMMARIZATION OF SEARCH RESULTS USING CONCEPT BAS...cseij
Query sensitive summarization aims at providing the users with the summary of the contents of single or multiple web pages based on the search query. This paper proposes a novel idea of generating a comparative summary from a set of URLs from the search result. User selects a set of web page links from the search result produced by search engine. Comparative summary of these selected web sites is generated. This method makes use of HTML DOM tree structure of these web pages. HTML documents are segmented into set of concept blocks. Sentence score of each concept block is computed with respect to the query and feature keywords. The important sentences from the concept blocks of different web pages are extracted to compose the comparative summary on the fly. This system reduces the time and effort required for the user to browse various web sites to compare the information. The comparative summary of the contents would help the users in quick decision making.
Not Good Enough but Try Again! Mitigating the Impact of Rejections on New Con...Aleksi Aaltonen
Presentation at the University of Miami on 3 December 2021 on how Stack Overflow improved the retention of new contributors whose initial question is rejected (closed) as substandard. The presentation is based on a paper coauthored with Sunil Wattal.
The activity of finding significant data identified with a particular subject is troublesome in web because of the immensity of web information. This situation makes website streamlining strategies into an irreplaceable technique according to analysts, academicians, and industrialists. Inquiry history investigation is the definite examination of web information from various clients with the end goal of comprehension and upgrading web taking care of. Inquiry log or client seek history incorporates clients' beforehand submitted inquiries and their comparing clicked reports or locales' URLs. Accordingly question log investigation is considered as the most utilized technique for improving the clients' pursuit encounter. The proposed strategy investigates and groups client scan histories with the end goal of website streamlining. In this approach, the issue of getting sorted out clients' verifiable questions into bunches in a dynamic and robotized design is examined. The consequently arranged inquiry gatherings will help in various website streamlining systems like question proposal, item re-positioning, question adjustments and so on. The proposed strategy considers a question aggregate as an accumulation of inquiries together with the comparing set of clicked URLs that are identified with each other around a general data require. This technique proposes another strategy for joining word likeness measures alongside report similitude measures to frame a consolidated comparability measure. In the proposed strategy other question importance measures, for example, inquiry reformulation and clicked URL idea are likewise considered. Assessment comes about show how the proposed technique outflanks existing strategies.
Context Sensitive Search String Composition Algorithm using User Intention to...IJECEIAES
Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.
QUERY SENSITIVE COMPARATIVE SUMMARIZATION OF SEARCH RESULTS USING CONCEPT BAS...cseij
Query sensitive summarization aims at providing the users with the summary of the contents of single or multiple web pages based on the search query. This paper proposes a novel idea of generating a comparative summary from a set of URLs from the search result. User selects a set of web page links from the search result produced by search engine. Comparative summary of these selected web sites is generated. This method makes use of HTML DOM tree structure of these web pages. HTML documents are segmented into set of concept blocks. Sentence score of each concept block is computed with respect to the query and feature keywords. The important sentences from the concept blocks of different web pages are extracted to compose the comparative summary on the fly. This system reduces the time and effort required for the user to browse various web sites to compare the information. The comparative summary of the contents would help the users in quick decision making.
Not Good Enough but Try Again! Mitigating the Impact of Rejections on New Con...Aleksi Aaltonen
Presentation at the University of Miami on 3 December 2021 on how Stack Overflow improved the retention of new contributors whose initial question is rejected (closed) as substandard. The presentation is based on a paper coauthored with Sunil Wattal.
The activity of finding significant data identified with a particular subject is troublesome in web because of the immensity of web information. This situation makes website streamlining strategies into an irreplaceable technique according to analysts, academicians, and industrialists. Inquiry history investigation is the definite examination of web information from various clients with the end goal of comprehension and upgrading web taking care of. Inquiry log or client seek history incorporates clients' beforehand submitted inquiries and their comparing clicked reports or locales' URLs. Accordingly question log investigation is considered as the most utilized technique for improving the clients' pursuit encounter. The proposed strategy investigates and groups client scan histories with the end goal of website streamlining. In this approach, the issue of getting sorted out clients' verifiable questions into bunches in a dynamic and robotized design is examined. The consequently arranged inquiry gatherings will help in various website streamlining systems like question proposal, item re-positioning, question adjustments and so on. The proposed strategy considers a question aggregate as an accumulation of inquiries together with the comparing set of clicked URLs that are identified with each other around a general data require. This technique proposes another strategy for joining word likeness measures alongside report similitude measures to frame a consolidated comparability measure. In the proposed strategy other question importance measures, for example, inquiry reformulation and clicked URL idea are likewise considered. Assessment comes about show how the proposed technique outflanks existing strategies.
Our evaluation reveals that there is a preference for certain features depending on the search task. In addition, we touch on the current pain point of faceted search: the acquisition of faceted subject metadata for unstructured documents. We found a strong preference for prototypes displaying just a few facets generated based on either the query or the matching documents.
Search Interface Feature Evaluation in BiosciencesZanda Mark
Read more here: http://pingar.com/
This paper reports findings on desirable interface features for different
search tasks in the biomedical domain. We conducted a user study where
we asked bioscientists to evaluate the usefulness of autocomplete, query
expansions, faceted refinement, related searches and results preview
implementations in new pilot interfaces and publicly available systems
while using baseline and their own queries. Our evaluation reveals that
there is a preference for certain features depending on the search task.
In addition, we touch on the current pain point of faceted search: the
acquisition of faceted subject metadata for unstructured documents.
We found a strong preference for prototypes displaying just a few facets
generated based on either the query or the matching documents.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
An Efficient Approach for Keyword Selection ; Improving Accessibility of Web ...dannyijwest
General search engines often provide low precise results even for detailed queries. So there is a vital need
to elicit useful information like keywords for search engines to provide acceptable results for user’s search
queries. Although many methods have been proposed to show how to extract keywords automatically, all
attempt to get a better recall, precision and other criteria which describe how the method has done its job
as an author. This paper presents a new automatic keyword extraction method which improves accessibility
of web content by search engines. The proposed method defines some coefficients determining features
efficiency and tries to optimize them by using a genetic algorithm. Furthermore, it evaluates candidate
keywords by a function that utilizes the result of search engines. When comparing to the other methods,
experiments demonstrate that by using the proposed method, a higher score is achieved from search
engines without losing noticeable recall or precision.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Text preprocessing is a vital stage in text classification (TC) particularly and text mining generally. Text preprocessing tools is to reduce multiple forms of the word to one form. In addition, text preprocessing techniques are provided a lot of significance and widely studied in machine learning. The basic phase in text classification involves preprocessing features, extracting relevant features against the features in a database. However, they have a great impact on reducing the time requirement and speed resources needed. The effect of the preprocessing tools on English text classification is an area of research. This paper provides an evaluation study of several preprocessing tools for English text classification. The study includes using the raw text, the tokenization, the stop words, and the stemmed. Two different methods chi-square and TF-IDF with cosine similarity score for feature extraction are used based on BBC English dataset. The Experimental results show that the text preprocessing effect on the feature extraction methods that enhances the performance of English text classification especially for small threshold values.
An Advanced IR System of Relational Keyword Search Techniquepaperpublications3
Abstract: Now these days keyword search to relational data set becomes an area of research within the data base and Information Retrieval. There is no standard process of information retrieval, which will clearly show the accurate result also it shows keyword search with ranking. Execution time is retrieving of data is more in existing system. We propose a system for increasing performance of relational keyword search systems. In the proposed system we combine schema-based and graph-based approaches and propose a Relational Keyword Search System to overcome the mentioned disadvantages of existing systems and manage the information and user access the information very efficiently. Keyword Search with the ranking requires very low execution time. Execution time of retrieving information and file length during Information retrieval can be display using chart.Keywords: Keyword Search, Datasets, Information Retrieval Query Workloads, Schema-based Systems, Graph-based Systems, ranking, relational databases.
Title: An Advanced IR System of Relational Keyword Search Technique
Author: Dhananjay A. Gholap, Gumaste S. V
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
Our evaluation reveals that there is a preference for certain features depending on the search task. In addition, we touch on the current pain point of faceted search: the acquisition of faceted subject metadata for unstructured documents. We found a strong preference for prototypes displaying just a few facets generated based on either the query or the matching documents.
Search Interface Feature Evaluation in BiosciencesZanda Mark
Read more here: http://pingar.com/
This paper reports findings on desirable interface features for different
search tasks in the biomedical domain. We conducted a user study where
we asked bioscientists to evaluate the usefulness of autocomplete, query
expansions, faceted refinement, related searches and results preview
implementations in new pilot interfaces and publicly available systems
while using baseline and their own queries. Our evaluation reveals that
there is a preference for certain features depending on the search task.
In addition, we touch on the current pain point of faceted search: the
acquisition of faceted subject metadata for unstructured documents.
We found a strong preference for prototypes displaying just a few facets
generated based on either the query or the matching documents.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
An Efficient Approach for Keyword Selection ; Improving Accessibility of Web ...dannyijwest
General search engines often provide low precise results even for detailed queries. So there is a vital need
to elicit useful information like keywords for search engines to provide acceptable results for user’s search
queries. Although many methods have been proposed to show how to extract keywords automatically, all
attempt to get a better recall, precision and other criteria which describe how the method has done its job
as an author. This paper presents a new automatic keyword extraction method which improves accessibility
of web content by search engines. The proposed method defines some coefficients determining features
efficiency and tries to optimize them by using a genetic algorithm. Furthermore, it evaluates candidate
keywords by a function that utilizes the result of search engines. When comparing to the other methods,
experiments demonstrate that by using the proposed method, a higher score is achieved from search
engines without losing noticeable recall or precision.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Text preprocessing is a vital stage in text classification (TC) particularly and text mining generally. Text preprocessing tools is to reduce multiple forms of the word to one form. In addition, text preprocessing techniques are provided a lot of significance and widely studied in machine learning. The basic phase in text classification involves preprocessing features, extracting relevant features against the features in a database. However, they have a great impact on reducing the time requirement and speed resources needed. The effect of the preprocessing tools on English text classification is an area of research. This paper provides an evaluation study of several preprocessing tools for English text classification. The study includes using the raw text, the tokenization, the stop words, and the stemmed. Two different methods chi-square and TF-IDF with cosine similarity score for feature extraction are used based on BBC English dataset. The Experimental results show that the text preprocessing effect on the feature extraction methods that enhances the performance of English text classification especially for small threshold values.
An Advanced IR System of Relational Keyword Search Techniquepaperpublications3
Abstract: Now these days keyword search to relational data set becomes an area of research within the data base and Information Retrieval. There is no standard process of information retrieval, which will clearly show the accurate result also it shows keyword search with ranking. Execution time is retrieving of data is more in existing system. We propose a system for increasing performance of relational keyword search systems. In the proposed system we combine schema-based and graph-based approaches and propose a Relational Keyword Search System to overcome the mentioned disadvantages of existing systems and manage the information and user access the information very efficiently. Keyword Search with the ranking requires very low execution time. Execution time of retrieving information and file length during Information retrieval can be display using chart.Keywords: Keyword Search, Datasets, Information Retrieval Query Workloads, Schema-based Systems, Graph-based Systems, ranking, relational databases.
Title: An Advanced IR System of Relational Keyword Search Technique
Author: Dhananjay A. Gholap, Gumaste S. V
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
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,
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journal of engineering, online Submission
Supporting Exploratory People Search: A Study of Factor Transparency and User...Shuguang Han
People search is an active research topic in recent years. Related works includes expert finding, collaborator recommendation, link prediction and social matching. However, the diverse objectives and exploratory nature of those tasks make it difficult to develop a flexible method for people search that works for every task. In this project, we developed PeopleExplorer, an interactive people search system to support exploratory search tasks when looking for people. In the system, users could specify their task objectives by selecting and adjusting key criteria. Three criteria were considered: the content relevance, the candidate authoritativeness and the social similarity between the user and the candidates. This project represents a first attempt to add transparency to exploratory people search, and to give users full control over the search process. The system was evaluated through an experiment with 24 participants undertaking four different tasks. The results show that with comparable time and effort, users of our system performed significantly better in their people search tasks than those using the baseline system. Users of our system also exhibited many unique behaviors in query reformulation and candidate selection. We found that users’ general perceptions about three criteria varied during different tasks, which confirms our assumptions regarding modeling task difference and user variance in people search systems.
Structure, Personalization, Scale: A Deep Dive into LinkedIn SearchC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1Gel2jo.
The authors discuss some of the unique challenges they've faced delivering highly personalized search over semi-structured data at massive scale. Filmed at qconnewyork.com.
Asif Makhani heads Search at LinkedIn. Prior to that, he was a founding member of A9 and led the development and launch of Amazon CloudSearch. Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google.
Web search engines help users find useful information on the WWW. However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search results should be adapted to users with different information needs. So, there is need of
several approaches to adapting search results according to each user’s need for relevant information without any user effort. Such search systems that adapt to each user’s preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user’s browsing history. There are three possible types of web search system which can provide personalized information: (1) systems using relevance feedback, (2) systems in which users register their interest, and (3) systems that recommend information based on user’s history. In first technique, users have to provide feedback on relevant or irrelevant judgments which is time consuming and the second one needs
registration of users with their static interests which need extra effort from user. So, the third technique is best in which users don’t have to give explicit rating; relevancy automatically tracked by user behavior with search results and history of data usage. It doesn’t require registration of interests; it captures changing interests of user dynamically by itself. The result section shows that user’s browsing history allows each user to perform more fine-grained search by capturing changes of each user’s
preferences without any user effort. Users need less time to find the relevant snippet in personalized
search results compared to original results.
Web search engines help users find useful information on the WWW. However, when the same
query is submitted by different users, typical search engines return the same result regardless of who
submitted the query. Generally, each user has different information needs for his/her query. Therefore,
the search results should be adapted to users with different information needs. So, there is need of
several approaches to adapting search results according to each user’s need for relevant information
without any user effort. Such search systems that adapt to each user’s preferences can be achieved by
constructing user profiles based on modified collaborative filtering with detailed analysis of user’s
browsing history.
There are three possible types of web search system which can provide personalized
information: (1) systems using relevance feedback, (2) systems in which users register their interest, and
(3) systems that recommend information based on user’s history. In first technique, users have to provide
feedback on relevant or irrelevant judgments which is time consuming and the second one needs
registration of users with their static interests which need extra effort from user. So, the third technique
is best in which users don’t have to give explicit rating; relevancy automatically tracked by user
behavior with search results and history of data usage. It doesn’t require registration of interests; it
captures changing interests of user dynamically by itself. The result section shows that user’s browsing
history allows each user to perform more fine-grained search by capturing changes of each user’s
preferences without any user effort. Users need less time to find the relevant snippet in personalized
search results compared to original results
User behavior model & recommendation on basis of social networks Shah Alam Sabuj
At present social networks play an important role to express people's sentiment and interest in a particular field. Extracting a user's public social network data (what the user shares with friends and relatives and how the user reacts over others' thought) means extracting the user's behavior. Defining some determined hypothesis if we make machine understand human sentiment and interest, it is possible to recommend a user about his/her personal interest on basis of the user's sentiment analyzed by machine. Our main approach is to suggest a user regarding the user's specific interest that is anticipated by analyzing the user's public data. This can be extended to further business analysis to suggest products or services of different companies depending on the consumer's personal choice. This automation will also help to choose the correct candidate for any questionnaire. This system will also help anyone to know about himself or herself, how one's behavior may influence others. It is possible to identify different types of people such as- dependable people, leadership skilled, people of supportive mentality, people of negative mentality etc.
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
SUPPORTING PRIVACY PROTECTION IN PERSONALIZED WEB SEARCHnikhil421080
Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidence shows that users’ reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS.
We study privacy protection in PWS applications that model user preferences as hierarchical user profiles. We propose a PWS framework called UPS that can adaptively generalize profiles by queries while respecting user-specified privacy requirements. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile.
We present two greedy algorithms, namely GreedyDP and GreedyIL, for runtime generalization. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. Extensive experiments demonstrate the effectiveness of our framework. The experimental results also reveal that GreedyIL significantly outperforms GreedyDP in terms of efficiency.
Web search engines (e.g. Google, Yahoo, Microsoft Live Search, etc.) are widely used to find certain data among a huge amount of information in a minimal amount of time. These useful tools also pose a privacy threat to users. Web search engines profile their users on the basis of past searches submitted by them. In the proposed system, we can implement the String Similarity Match Algorithm (SSM Algorithm) for improving better search quality results. To address this privacy threat, current solutions propose new mechanisms that introduce a high cost in terms of computation and communication. Personalized search is a promising way to improve the accuracy of web searches. However, effective personalized search requires collecting and aggregating user information, which often raises serious concerns of privacy infringement for many users. Indeed, these concerns have become one of the main barriers to deploying personalized search applications, and how to do privacy-preserving personalization is a great challenge. In this, we propose and try to resist adversaries with broader background knowledge, such as richer relationship among topics. Richer relationship means we generalize the user profile results by using the background knowledge which is going to store in history. Through this, we can hide the user search results. By using this mechanism, we can achieve privacy.
Personalized Search at Sandia National LabsLucidworks
Clay Pryor, R&D S&E, Computer Science & Ryan Cooper, Sandia National Labs. Presentation from ACTIVATE 2019, the Search and AI Conference hosted by Lucidworks. http://www.activate-conf.com
Considering users' behaviours in improving the responses of an informacion baseinscit2006
Babajide Afolabi and Odile Thiery
Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA) Campus Scientifique BP 239, 54506 Vandoeuvre-les-Nancy, France.
4 postsRe Topic 2 DQ 1Qualitative research produces a v.docxmeghanivkwserie
4 posts
Re: Topic 2 DQ 1
Qualitative research produces a variety of data, from a variety of sources. Data sources may be personal interviews (written or recorded), surveys, questionnaires, official documents or observation notes. To complicate matters, more often than not, there are numerous respondents or participants and multiple researchers. To extricate and code data from multiple data sources can be difficult, but made much easier if the data is organized appropriately. (Katherine B.2017)
The vast majority of qualitative data is "Unstructured Data," which includes documents, photographs, audio, and video.
The simplest things we can do to improve the usability of unstructured data for analysis are:
Convert it to a structured schema that can be evaluated with quantitative methods.
Make it simple to find.
On the first point, we can feed documents to full-text search engines such as Lucene, which make data retrieval simple. We can also design full text search engines to execute faceted searches, allowing us to attach Metadata facets (e.g., Author, Media Type, Creation Date, etc.) to enhance our quantitative research. The same search engine was used. (Bensal P and others…. 2010)
On the second point, there are a variety of methods for converting qualitative Unstructured Data into Structured Data (which may be quantitatively examined). But it all relies on what you want to do with the Structured Data and how you get it. You can, for example, create n-grams (continuous sequences of words) and then analyze those n-grams to identify what the most common terms are within a subset of texts.
You might wish to have someone manually transcribe all consumer references of a product when evaluating footage. There are already Machine Learning algorithms that can transcribe and recognize speech.
Machine Learning and Deep Learning programs that can extract usable and reliable quantitative data from qualitative data will be extremely important in the future of analytics. However, manual methods such as employing Amazon Mechanical Turk, or a combination of both, are equally viable options for extracting Quantitative Structured Data from Qualitative Unstructured Data.
Using 200-300 APA FORMAT with references to support this discussion,
Qualitative data has been described as voluminous and sometimes overwhelming to the researcher. Discuss two strategies that would help a researcher manage and organize the data.
.
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In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
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🔍 Increased frequency and complexity of cyber threats.
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Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Temporal Latent Topic User Profiles for Search Personalisation
1. Thanh Vu, Alistair Willis,
Dawei Song
The Open University, UK
Temporal Latent Topic User Profiles for
Search Personalisation
Son N. Tran
City London University
The 37th European Conference on Information
Retrieval
st
2. Search Personalisation
2
Return search results based on
The input query
The user searching interests
Different users submit the same input query will
probably get different search result lists
Even an individual user will get different search
results at different search times (e.g., Open US)
Temporal Latent Topic User Profiles for Search
Personalisation
3. The performance of search
personalisation
depends on
the richness of a user profile
J. Teevan, M. R. Morris, and S. Bush. Discovering and using groups to improve personalized search. In WSDM’2009
3
Temporal Latent Topic User Profiles for Search
Personalisation
4. Topic-based user profiles
4
Use Human generated ontology (ODP –
dmoz.org) to extract topics from all
clicked/relevant documents of a specific user to
build her profile
1. R. W. White, et al., Enhancing Personalized Search by Mining and Modeling Task Behavior. In WWW’2013
2. P. N. Bennett, et al., Modeling the impact of short- and long-term behavior on search personalization. In SIGIR’2012
Temporal Latent Topic User Profiles for Search
Personalisation
5. Challenges for Human Generated
Ontology
5
New topics which are not covered in the Ontology
will possibly emerge overtime
Expensive human effort to classify/maintain each
document into correct categories
Temporal Latent Topic User Profiles for Search
Personalisation
6. Challenges for Time-awareness
6
Previous methods use all the clicked/relevant
documents of a user to build her searching profile
The documents are treated equally without
considering temporal features (i.e., the time of
documents being clicked and viewed)
The profile is too broad
Cannot fully express the current interest of the user
1. T. T. Vu, et al., Improving search personalisation with dynamic group formation. In SIGIR’2014
2. K. Raman, et al., Toward whole-session relevance: Exploring intrinsic diversity in web search. In SIGIR’2013
Temporal Latent Topic User Profiles for Search
Personalisation
7. Research Questions
7
1. How can we build user profiles with time-
awareness?
2. Do the time-aware profiles help improve search
performance?
Temporal Latent Topic User Profiles for Search
Personalisation
9. Building temporal latent topic user
profiles (1)
9
Non-temporal method
Temporal Latent Topic User Profiles for Search
Personalisation
4th 1st2nd3rd
Football
Law
Health
OS
0.51
0.33
0.11
0.05
Clicked documents
Football
Law
OS
Health
0.55
0.27
0.10
0.08
Law
OS
Health
Football
0.41
0.37
0.12
0.10
OS
Law
Football
Health
0.65
0.21
0.10
0.04
Distribution over topics
Football
Law
OS
Health
0.32
0.30
0.29
0.09
Means over topics
The topic-based user profile
10. Building temporal latent topic user
profiles (2)
10
Our method
Temporal Latent Topic User Profiles for Search
Personalisation
1st
Football
Law
Health
OS
0.51
0.33
0.11
0.05
Football
Law
Health
OS
0.51
0.33
0.11
0.05
The temporal topic user profile
0.90
11. Football
Law
Health
OS
0.53
0.30
0.09
0.08
Building temporal latent topic user
profiles (2)
11
Temporal Latent Topic User Profiles for Search
Personalisation
2nd 1st
Football
Law
Health
OS
0.51
0.33
0.11
0.05
Football
Law
OS
Health
0.55
0.27
0.10
0.08
The temporal topic user profile
0.91 0.90
12. Football
Law
OS
Health
0.37
0.34
0.19
0.10
0.910.92
Building temporal latent topic user
profiles (2)
12
Temporal Latent Topic User Profiles for Search
Personalisation
3rd 1st2nd
Football
Law
Health
OS
0.51
0.33
0.11
0.05
Football
Health
OS
Law
0.55
0.27
0.10
0.08
Law
OS
Health
Football
0.41
0.37
0.12
0.10
The temporal topic user profile
0.90
13. OS
Law
Football
Health
0.32
0.30
0.29
0.09
Building temporal latent topic user
profiles (2)
13
Temporal Latent Topic User Profiles for Search
Personalisation
4th 1st2nd3rd
Football
Law
Health
OS
0.51
0.33
0.11
0.05
Football
Health
OS
Law
0.55
0.27
0.10
0.08
Law
OS
Health
Football
0.41
0.37
0.12
0.10
OS
Law
Football
Health
0.65
0.21
0.10
0.04
Temporal topic profile
0.93
0.92 0.91
0.90
Football
Law
OS
Health
0.32
0.30
0.29
0.09
Non-temporal topic profile
14. Building temporal latent topic user
profiles (3)
Temporal Latent Topic User Profiles for Search
Personalisation14
Du = {d1, d2, …, dn} is a relevant document set of
the user u
The user profile of u is a distribution over the
topic Z (extracted by LDA)
tdi = n indicates that di is the nth most
relevant/clicked document of u
α is the decay parameter; K is the normalisation
factor
15. Building temporal latent topic user
profiles (4)
15
Long-term user profile
Use relevant documents extracted from the user’s
whole search history
Daily user profile
Use relevant documents extracted from the search
history of the user in the current searching day
Session user profile
Use relevant documents extracted from the search
history of the user in the current search session
Temporal Latent Topic User Profiles for Search
Personalisation
16. Re-ranking search results (1)
16
Temporal Latent Topic User Profiles for Search
Personalisation
1 32
Health
Law
Football
OS
0.51
0.33
0.11
0.05
Football
Law
Health
OS
0.55
0.27
0.13
0.05
Football
OS
Health
Law
0.41
0.37
0.12
0.10
Original Rank
132
Health
Law
Football
OS
0.51
0.33
0.11
0.05
Football
Law
Health
OS
0.55
0.27
0.13
0.05
Football
OS
Health
Law
0.41
0.37
0.12
0.10
After re-ranking
Football
Law
OS
Health
0.47
0.24
0.16
0.12
The user profile (p)
17. Re-ranking search results (2)
17
Personalised scores
Use Jensen-Shannon divergence (DJS[d||p] )
Temporal Latent Topic User Profiles for Search
Personalisation
1 32
Health
Law
Football
OS
0.51
0.33
0.11
0.05
Football
Law
Health
OS
0.55
0.27
0.13
0.05
Football
OS
Health
Law
0.41
0.37
0.12
0.10
Football
Law
OS
Health
0.47
0.24
0.16
0.12
Returned documents (d)
The user profile (p)
18. Re-ranking search results (3)
18
Re-ranking Features
Re-Ranking Algorithm: LambdaMART[1]
1. C. J. Burges, et al., Learning to rank with non-smooth cost functions. In NIPS’2007.
Feature Description
Personalised Features
LongTermScore Personalised score between document and long-term
profile
DailyScore Personalised score between document and daily profile
SessionScore Personalised score between document and session
profile
Non-personalised Features
DocRank Rank of document on original returned list
QuerySim Cosine similarity score between current and previous
queries
QueryNo Total number of queries that have been submitted in the
current search session (included the current query)
19. Evaluation
19
Dataset
The query logs of 1166 anonymous users in four
weeks, from 01st to 28th July 2012
A log entity consists of an anonymous user
identifier, a query, top-10 returned URLs, and
clicked documents along with the user’s dwell
time
Download all the URLs’ content for learning topics
A search session is demarcated by 30 minutes of
user inactivity
A relevant document is a click with dwell time of
at least 30 seconds or the last click in a session
(SAT click)Temporal Latent Topic User Profiles for Search
Personalisation
20. Evaluation methodology
20
Assign a positive (relevant) label to a returned
URL if
it is a SAT click in the current query
it is a SAT click in one of the other repeated queries
in the same search session
Assign negative (irrelevant) labels to the rest of
URLs
Temporal Latent Topic User Profiles for Search
Personalisation
21. Personalisation Methods and
Baselines
21
Personalisation Methods
LON uses only LongTermScore from long-term
profile
DAI uses only DailyScore from daily profile
SES uses SessionScore from session profile
ALL uses all personalised scores from three
profiles (ALL)
Baselines
Default is the default ranking returned by the
search engine
Static uses the LongTermScore from long-term
profile without time-awareness (i.e., not using decay
function)
Temporal Latent Topic User Profiles for Search
Personalisation
22. Results
22
Evaluation metrics
Mean Average Precision (MAP)
Precision (P@k)
Mean Reciprocal Rank (MRR)
Normalized Discounted Cumulative Gain
(nDCG@k)
For each evaluation metric, the higher value
indicates the better ranking
Temporal Latent Topic User Profiles for Search
Personalisation
23. Overall Performance
23
Temporal Latent Topic User Profiles for Search
Personalisation
• All the improvements over the baselines
are all significant with paired t-test of p <
0.001
24. • Three temporal profiles help to improve
search performance over default ranking
and the use of non-temporal profile
Conclusions (1)
24
Temporal Latent Topic User Profiles for Search
Personalisation
25. • Using all features (ALL) achieves the
highest performance
Conclusions (2)
25
Temporal Latent Topic User Profiles for Search
Personalisation
26. Conclusions (3)
26
Temporal Latent Topic User Profiles for Search
Personalisation
• The session profile achieves better
performance than the daily profile
• The daily profile gains advantages over
the long-term profile
27. Conclusions (4)
27
Temporal Latent Topic User Profiles for Search
Personalisation
• Without time-awareness, the long-term
profile gets no improvement over the
default ranking
28. Summary
Temporal Latent Topic User Profiles for Search
Personalisation28
Build long-term, daily and session profiles with
time-awareness using topics extracted
automatically from relevant documents in different
time scales
Use the three profiles to re-rank search results
returned by Bing and show the significant
improvement in search performances
31. Example of query logs
31
Temporal Latent Topic User Profiles for Search
Personalisation
32. Click Entropies
32
P(d|q) is the percentage of the clicks on
document d among all the clicks for q
A smaller query click entropy value indicates
more agreement between users on clicking a
small number of web pages
Temporal Latent Topic User Profiles for Search
Personalisation
34. Query Positions in Search
Session
34
Aim to study whether the position of a query has
any effect on the performance of the temporal
latent topic profiles
Label the queries by their positions during the
search
Temporal Latent Topic User Profiles for Search
Personalisation
35. Temporal Latent Topic User Profiles for Search
Personalisation35
Footbal
l
Law
Health
OS
0.51
0.33
0.11
0.05
Clicked documents
Footbal
l
Health
OS
Law
0.55
0.27
0.13
0.05
Law
OS
Health
Footbal
l
0.41
0.37
0.12
0.10
OS
Law
Footbal
l
Health
0.65
0.15
0.11
0.09
Distribution over topics
Footbal
l
Law
OS
Health
0.32
0.29
0.28
0.11
Means over topics
The topic-based user
profile
36. Re-ranking search results (1)
Temporal Latent Topic User Profiles for Search
Personalisation36
Query: MU
37. Pre-processing
37
Remove the queries whose positive label set is
empty from the dataset
Discard the domain-related queries (e.g.,
Facebook, Youtube)
Temporal Latent Topic User Profiles for Search
Personalisation
Editor's Notes
Use the rank positions of the positive label as the ground truth to evaluate the search performance before and after re-ranking
The session profile (SES) achieves better performance than the daily profile (DAI). It also shows that the daily profile (DAI) gains advantage over the long-term profile (LON). This indicates that the short-term profiles capture more details of user interest than the longer ones.
The combination of all features (ALL) achieves the highest performance.
The session profile (SES) achieves better performance than the daily profile (DAI). It also shows that the daily profile (DAI) gains advantage over the long-term profile (LON). This indicates that the short-term profiles capture more details of user interest than the longer ones.
The combination of all features (ALL) achieves the highest performance.
The session profile (SES) achieves better performance than the daily profile (DAI). It also shows that the daily profile (DAI) gains advantage over the long-term profile (LON). This indicates that the short-term profiles capture more details of user interest than the longer ones.
The combination of all features (ALL) achieves the highest performance.
The session profile (SES) achieves better performance than the daily profile (DAI). It also shows that the daily profile (DAI) gains advantage over the long-term profile (LON). This indicates that the short-term profiles capture more details of user interest than the longer ones.
The combination of all features (ALL) achieves the highest performance.
The session profile (SES) achieves better performance than the daily profile (DAI). It also shows that the daily profile (DAI) gains advantage over the long-term profile (LON). This indicates that the short-term profiles capture more details of user interest than the longer ones.
The combination of all features (ALL) achieves the highest performance.
Show the improvement of the temporal profiles over the Default baseline using MAP metric for different magnitudes of click entropy
we show the improvement of the temporal profiles over the Default ranking from the search engine in term of MAP metric for different magnitudes of click entropy. Here the statistical significance is also guaranteed with the use of paired t-test (p < 0:001).
With smaller value of click entropy, the re-ranking performance is only slightly improved. For example, with click entropy between 0 and 0.5, the improvement of the MAP metric from long-term profile is of only 0.39%, in comparison with the original search engine. One may see that the effectiveness of the temporal pro les is increasing proportionally according to the value of click entropy.
The highest improvements are achieved when click entropies are >= 2
A query usually has a broader influence in a search session than only returning a list of URLs. The position of a query in a search session is also important because it may be fine-tuned by a user after the unsatisfactory results from previous queries.
In this experiment we aim to study whether the position of a query has any effect on the performance of the temporal latent topic profiles.
For each session, we label the queries by their positions during the search