This document summarizes an article from the International Journal of Engineering Research and Applications titled "Explicit User Profiles for Semantic Web Search Using XML" by C. Srinvas.
The article discusses how to build explicit user profiles using XML to personalize semantic web searches. It covers collecting user data, inferring user profiles, and representing profiles. The key approach is to define an ontology of topics and annotate concepts with interest scores that are continuously updated based on user behavior using spreading activation methods. This builds ontological user profiles that represent user interests to improve search accuracy.
A Least Square Approach ToAnlayze Usage Data For Effective Web PersonalizationIDES Editor
Web server logs have abundant information about
the nature of users accessing it. The analysis of the users
current interestbased on the navigational behavior may help
the organizationsto guide the users in their browsing activity
and obtain relevantinformation in a shorter span of time
[1].Web usage mining is used to discover interesting user
navigationpatterns and can be applied to many real-world
problems, such as improving Web sites/pages,
makingadditional topic or product recommendations, user/
customer behavior studies, etc [23].Web usage mining, in
conjunction with standard approaches to personalization
helps to address some of the shortcomings of these
techniques, including reliance on subjective lack of
scalability, poor performance, user ratings and sparse
data[2,3,4,5,6]. But, it is not sufficient to discover patterns
from usage data for performing the personalization tasks.
It is necessary to derive a good quality of aggregate usage
profiles which indeed will help to devise efficient
recommendation for web personalization [11, 12, 13].Also
the unsupervised and competitive learning algorithms has
help to efficiently cluster user based access patterns by
mining web logs [19, 20, 24].
This paper presents and experimentally evaluates a
technique for finely tuninguser clusters based on similar
web access patterns on their usage profiles by approximating
through least square approach. Each cluster is having users
with similar browsing patterns. These clusters are useful
in web personalization so that it communicates better with
its users.Experimental results indicate thatusing the
generated aggregate usage profiles with approximating
clusters through least square approach effectively
personalize at early stages of user visits to a site without
deeper knowledge about them.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
Classifying web users in a personalised search setup is cumbersome due the very nature of dynamism in
user browsing history. This fluctuating nature of user behaviour and user interest shall be well interpreted
within a fuzzy setting. Prior to analysing user behaviour, nature of user interests has to be collected. This
work proposes a fuzzy based user classification model to suit a personalised web search environment. The
user browsing data is collected using an established customised browser designed to suit personalisation.
The data are fuzzified and fuzzy rules are generated by applying decision trees. Using fuzzy rules, the
search pages are labelled to aid grouping of user search interests. Evaluation of the proposed approach
proves to be better when compared with Bayesian classifier.
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
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A Least Square Approach ToAnlayze Usage Data For Effective Web PersonalizationIDES Editor
Web server logs have abundant information about
the nature of users accessing it. The analysis of the users
current interestbased on the navigational behavior may help
the organizationsto guide the users in their browsing activity
and obtain relevantinformation in a shorter span of time
[1].Web usage mining is used to discover interesting user
navigationpatterns and can be applied to many real-world
problems, such as improving Web sites/pages,
makingadditional topic or product recommendations, user/
customer behavior studies, etc [23].Web usage mining, in
conjunction with standard approaches to personalization
helps to address some of the shortcomings of these
techniques, including reliance on subjective lack of
scalability, poor performance, user ratings and sparse
data[2,3,4,5,6]. But, it is not sufficient to discover patterns
from usage data for performing the personalization tasks.
It is necessary to derive a good quality of aggregate usage
profiles which indeed will help to devise efficient
recommendation for web personalization [11, 12, 13].Also
the unsupervised and competitive learning algorithms has
help to efficiently cluster user based access patterns by
mining web logs [19, 20, 24].
This paper presents and experimentally evaluates a
technique for finely tuninguser clusters based on similar
web access patterns on their usage profiles by approximating
through least square approach. Each cluster is having users
with similar browsing patterns. These clusters are useful
in web personalization so that it communicates better with
its users.Experimental results indicate thatusing the
generated aggregate usage profiles with approximating
clusters through least square approach effectively
personalize at early stages of user visits to a site without
deeper knowledge about them.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTALcscpconf
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
Contextual model of recommending resources on an academic networking portalcsandit
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
Classifying web users in a personalised search setup is cumbersome due the very nature of dynamism in
user browsing history. This fluctuating nature of user behaviour and user interest shall be well interpreted
within a fuzzy setting. Prior to analysing user behaviour, nature of user interests has to be collected. This
work proposes a fuzzy based user classification model to suit a personalised web search environment. The
user browsing data is collected using an established customised browser designed to suit personalisation.
The data are fuzzified and fuzzy rules are generated by applying decision trees. Using fuzzy rules, the
search pages are labelled to aid grouping of user search interests. Evaluation of the proposed approach
proves to be better when compared with Bayesian classifier.
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
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
A survey on ontology based web personalizationeSAT Journals
Abstract Over the last decade the data on World Wide Web has been growing in an exponential manner. According to Google the data is accelerating with a speed of billion pages per day [24]. Internet has around 2 million users accessing the World Wide Web for various information [25].These numbers certainly raise a severe concern over information over load challenges for the users. Many researchers have been working to overcome the challenge with web personalization, many researchers are looking at ontology based web personalization as an answer to the information overload, as each individual is unique. In this paper we present an overview of ontology based web personalization, Challenges and a survey of the work. This paper also points future work in web personalization. Index Terms: Web Personalization, Ontology, User modeling, web usage mining.
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.
This paper describes the Collective Experience Engine (CEE), a system for indexing Experiential-
Knowledge about Web knowledge-sources (websites), and performing relative-experience calculations
between participants of the CEE. The CEE provides an in-browser interface to query the collective
experience of others participating in the CEE. This interface accepts a list of URLs, to which the CEE adds
additional information based on the Queryee's previously indexed Experiential-Knowledge. The core of the
CEE is its Experiential-Context Conversation (ECConversation) functionality, whereby an collection of a
person’s Web Experiential-Knowledge can be utilized to allow a real-world conversation-like exchange of
information to take place, including adjusting information-flow based on the Queryee's experiential
background and knowledge, and providing additional experientially-related knowledge integrated into the
answer from multiple selected 'experience donors'. A relative-experience calculation ensures that
information is retrieved only from relative-experts, to ensure sufficient additional information exists, but
that such information isn't too advanced for the Queryee to process. This paper gives an overview of the
CEE, and the underlying algorithms and data structures, and describes a system architecture and
implementation details.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
Information is overloaded in the Internet due to the unstable growth of information and it makes information search as complicate process. Recommendation System (RS) is the tool and largely used nowadays in many areas to generate interest items to users. With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Web recommendation systems assist the users to get the exact information and facilitate the information search easier. Web recommendation is one of the techniques of web personalization, which recommends web pages or items to the user based on the previous browsing history. But the tremendous growth in the amount of the available information and the number of visitors to web sites in recent years places some key challenges for recommender system. The recent recommender systems stuck with producing high quality recommendation with large information, resulting unwanted item instead of targeted item or product, and performing many recommendations per second for millions of user and items. To avoid these challenges a new recommender system technologies are needed that can quickly produce high quality recommendation, even for a very large scale problems. To address these issues we use two recommender system process using fuzzy clustering and collaborative filtering algorithms. Fuzzy clustering is used to predict the items or product that will be accessed in the future based on the previous action of user browsers behavior. Collaborative filtering recommendation process is used to produce the user expects result from the result of fuzzy clustering and collection of Web Database data items. Using this new recommendation system, it results the user expected product or item with minimum time. This system reduces the result of unrelated and unwanted item to user and provides the results with user interested domain.
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
Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
Scaling Down Dimensions and Feature Extraction in Document Repository Classif...ijdmtaiir
-In this study a comprehensive evaluation of two
supervised feature selection methods for dimensionality
reduction is performed - Latent Semantic Indexing (LSI) and
Principal Component Analysis (PCA). This is gauged against
unsupervised techniques like fuzzy feature clustering using
hard fuzzy C-means (FCM) . The main objective of the study is
to estimate the relative efficiency of two supervised techniques
against unsupervised fuzzy techniques while reducing the
feature space. It is found that clustering using FCM leads to
better accuracy in classifying documents in the face of
evolutionary algorithms like LSI and PCA. Results show that
the clustering of features improves the accuracy of document
classification
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.
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
Dynamic Organization of User Historical QueriesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This work describes a new system User Profile Relevant Results -
UProRevs which would filter the results given by a search engine based on the user’s profile.
“UProRevs - User Profile Relevant Results” has been published by the IEEE - Computer Society as the proceedings for the 10th International Conference on Information Technology.
20 Ideas for your Website Homepage ContentBarry Feldman
Perplexed about what to put on your website home? Every company deals with this tough challenge. The 20 ideas in this presentation should give you a strong starting point.
A survey on ontology based web personalizationeSAT Journals
Abstract Over the last decade the data on World Wide Web has been growing in an exponential manner. According to Google the data is accelerating with a speed of billion pages per day [24]. Internet has around 2 million users accessing the World Wide Web for various information [25].These numbers certainly raise a severe concern over information over load challenges for the users. Many researchers have been working to overcome the challenge with web personalization, many researchers are looking at ontology based web personalization as an answer to the information overload, as each individual is unique. In this paper we present an overview of ontology based web personalization, Challenges and a survey of the work. This paper also points future work in web personalization. Index Terms: Web Personalization, Ontology, User modeling, web usage mining.
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.
This paper describes the Collective Experience Engine (CEE), a system for indexing Experiential-
Knowledge about Web knowledge-sources (websites), and performing relative-experience calculations
between participants of the CEE. The CEE provides an in-browser interface to query the collective
experience of others participating in the CEE. This interface accepts a list of URLs, to which the CEE adds
additional information based on the Queryee's previously indexed Experiential-Knowledge. The core of the
CEE is its Experiential-Context Conversation (ECConversation) functionality, whereby an collection of a
person’s Web Experiential-Knowledge can be utilized to allow a real-world conversation-like exchange of
information to take place, including adjusting information-flow based on the Queryee's experiential
background and knowledge, and providing additional experientially-related knowledge integrated into the
answer from multiple selected 'experience donors'. A relative-experience calculation ensures that
information is retrieved only from relative-experts, to ensure sufficient additional information exists, but
that such information isn't too advanced for the Queryee to process. This paper gives an overview of the
CEE, and the underlying algorithms and data structures, and describes a system architecture and
implementation details.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
Information is overloaded in the Internet due to the unstable growth of information and it makes information search as complicate process. Recommendation System (RS) is the tool and largely used nowadays in many areas to generate interest items to users. With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Web recommendation systems assist the users to get the exact information and facilitate the information search easier. Web recommendation is one of the techniques of web personalization, which recommends web pages or items to the user based on the previous browsing history. But the tremendous growth in the amount of the available information and the number of visitors to web sites in recent years places some key challenges for recommender system. The recent recommender systems stuck with producing high quality recommendation with large information, resulting unwanted item instead of targeted item or product, and performing many recommendations per second for millions of user and items. To avoid these challenges a new recommender system technologies are needed that can quickly produce high quality recommendation, even for a very large scale problems. To address these issues we use two recommender system process using fuzzy clustering and collaborative filtering algorithms. Fuzzy clustering is used to predict the items or product that will be accessed in the future based on the previous action of user browsers behavior. Collaborative filtering recommendation process is used to produce the user expects result from the result of fuzzy clustering and collection of Web Database data items. Using this new recommendation system, it results the user expected product or item with minimum time. This system reduces the result of unrelated and unwanted item to user and provides the results with user interested domain.
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
Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
Scaling Down Dimensions and Feature Extraction in Document Repository Classif...ijdmtaiir
-In this study a comprehensive evaluation of two
supervised feature selection methods for dimensionality
reduction is performed - Latent Semantic Indexing (LSI) and
Principal Component Analysis (PCA). This is gauged against
unsupervised techniques like fuzzy feature clustering using
hard fuzzy C-means (FCM) . The main objective of the study is
to estimate the relative efficiency of two supervised techniques
against unsupervised fuzzy techniques while reducing the
feature space. It is found that clustering using FCM leads to
better accuracy in classifying documents in the face of
evolutionary algorithms like LSI and PCA. Results show that
the clustering of features improves the accuracy of document
classification
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.
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
Dynamic Organization of User Historical QueriesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This work describes a new system User Profile Relevant Results -
UProRevs which would filter the results given by a search engine based on the user’s profile.
“UProRevs - User Profile Relevant Results” has been published by the IEEE - Computer Society as the proceedings for the 10th International Conference on Information Technology.
20 Ideas for your Website Homepage ContentBarry Feldman
Perplexed about what to put on your website home? Every company deals with this tough challenge. The 20 ideas in this presentation should give you a strong starting point.
10 Insightful Quotes On Designing A Better Customer ExperienceYuan Wang
In an ever-changing landscape of one digital disruption after another, companies and organisations are looking for new ways to understand their target markets and engage them better. Increasingly they invest in user experience (UX) and customer experience design (CX) capabilities by working with a specialist UX agency or developing their own UX lab. Some UX practitioners are touting leaner and faster ways of developing customer-centric products and services, via methodologies such as guerilla research, rapid prototyping and Agile UX. Others seek innovation and fulfilment by spending more time in research, being more inclusive, and designing for social goods.
Experience is more than just an interface. It is a relationship, as well as a series of touch points between your brand and your customer. Here are our top 10 highlights and takeaways from the recent UX Australia conference to help you transform your customer experience design.
For full article, continue reading at https://yump.com.au/10-ways-supercharge-customer-experience-design/
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
Content personalisation is becoming more prevalent. A site, it's content and/or it's products, change dynamically according to the specific needs of the user. SEO needs to ensure we do not fall behind of this trend.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
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.
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.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
Certain Issues in Web Page Prediction, Classification and Clustering in Data ...IJAEMSJORNAL
Nowadays, data mining which is a part of web mining plays a vital role in various applications such as search engines, health care centers for extracting the individual patient details among huge database, analyzing disease based on basic criteria, education system for analyzing their performance level with other system, social networking, E-Commerce and knowledge management etc., which extract the information based on the user query. The issues are time taken to mine the target content or webpage from the search engines, space complexity and predicting the frequent webpage for the next user based on users’ behaviour.
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
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
Following the user’s interests in mobile context aware recommender systemsBouneffouf Djallel
The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2) how to model the user’s situation and its related interests? To the best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-ε-greedy algorithm we propose, which combines the standard ε-greedy algorithm and both content-based filtering and case-based reasoning techniques.
World Wide Web is a huge repository of information and there is a tremendous increase in the volume of
information daily. The number of users are also increasing day by day. To reduce users browsing time lot
of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are
applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range
of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many
others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort
of measure that can determine whether two objects are similar or dissimilar. In this paper a novel
clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various
performance measures of the proposed algorithm shows that the proposed method is a better method for
web log mining.
An Improvised Fuzzy Preference Tree Of CRS For E-Services Using Incremental A...IJTET Journal
Abstract—Web mining is the amalgamation of information accumulated by traditional data mining methodologies and techniques with information collected over the World Wide Web. A Recommendation system is a profound application that comforts the user in a decision-making process, where they lack of personal experience to choose an item from the confound set of alternative products or services. The key challenge in the development of recommender system is to overcome the problems like single level recommendation and static recommendation, which are exists in the real world e-services. The goal is to achieve and enhance predicting algorithm to discover the frequent items, which are feasible to be purchasable. At this point, we examine the prior buying patterns of the customers and use the knowledge thus procured, to achieve an item set, which co-ordinates with the purchasing mentality of a particular set of customers. Potential recommendation is concerned as a link structure among the items within E-commerce website, which supports the new customers to find related products in a hurry. In Existing system, a fuzzy set consists of user preference and item features alone, so the recommendations to the customers are irrelevant and anonymous. In this paper, we suggest a recommendation technique, which practices the wild spreading and data sharing competency of a huge customer linkage and also this method follows a fuzzy tree- structured model, in which fuzzy set techniques are utilized to express user preferences and purchased items are in a clustered form to develop a user convenient recommendations. Here, an incremental association rule mining is employed to find interesting relation between variables in a large database.
Framework for web personalization using web miningeSAT Journals
Abstract WWW is a large amount of information provider and a very big source of information. Users are increasing every day for accessing web sites. For efficient and effective handling, web mining coupled with suggestion techniques provides personalized contents at the disposal of users. Web Mining is an area of Data Mining dealing with the extraction of interesting knowledge from the Web. Here we are presenting a comprehensive overview of the personalization process based on Web usage mining. In this a host of Web usage mining activities required for this process, including the pre-processing and integration of data from multiple sources, and common pattern discovery techniques that are applied to the integrated usage data. Index Terms: Web-Usage Mining, Data Mining, Personalization, Pattern Discovery, Web Mining, Web Personalization
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.
FIND MY VENUE: Content & Review Based Location Recommendation System
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Explicit User Profiles for Semantic Web Search Using XML
C. Srinvas
Associate Professor & HOD, Dept of Information Technology,
Sree Visvesvaraya Institute of Technology & Science (SVITS), Mahabubnagar, Andhra Pradesh, India
Abstract
The web is diffusing day to day as the number of above mentioned sources of information as input to
users are increasing. Nowadays, end users always pattern discovery techniques; the system tailors the
interested to extract meaningful information provided content to the needs of each visitor of the
from the surplus of accessible Web resources on web site. The personalization process can result in
time. This leads to many problems such as the dynamic generation of recommendations, the
information overload, irrelevant information creation of index pages, the highlighting of existing
supply. Filtering irrelevant information is a key hyperlinks, the publishing of targeted
issue there are many solutions, among them advertisements or emails, etc
personalization of information services on the
Semantic Web offers a promising solution to Recommender Systems – A guidance based system
alleviate these problems, and to customize the tries to automatically recommend hyperlinks that are
web environment according to user’s information deemed to be relevant to the user’s interests, in
needs, interests and preferences. Explicit user order to facilitate access to the needed information
profiles are determined and maintained with the on a large website. It is usually implemented on the
help of XML. This helps the search engine Web server, and relies on data that reflects the
presenting the most relevant and expected results user’s interest implicitly (browsing history as
to the user. recorded in Web server logs) or explicitly (user
profile as entered through a registration form or
Key wards: User profiles, Semantic web, questionnaire). This approach will form the focus of
Personalization our overview of Web personalization.
Introduction Different Ways to Compute
Web personalization can be defined as any Recommendations
action that customizes the information or services Content-based filtering systems are solely based
provided by a web site to an individual user, or a set on individual users' preferences. The system tracks
of users, based on knowledge acquired by their each user's behaviour and recommends them items
navigational behavior, recorded in the web site’s that are similar to items the user liked in the past.
logs, in other words, its usage. This information is
often combined with the content and the structure of Collaborative filtering systems invite users to rate
the web site, as well as the interests/preferences of objects or divulge their preferences and interests and
the user, if they are available. The web then return information that is predicted to be of
personalization process is illustrated in Figure interest for them. This is based on the assumption
that users with similar behaviour (for example users
that rate similar objects) have analogous interests.
In rule-based filtering the users are asked to answer
to a set of questions. These questions are derived
from a decision tree, so as the user proceeds on
answering them, what she/he finally receives as a
result (for example a list of products) is tailored to
their needs. Content-based, rule-based and
collaborative filtering may also be used in
combination, for deducing more accurate
conclusions.
For example, lazy modeling is used in
collaborative filtering which simply stores all users’
information and then relies on K-Nearest-Neighbors
(KNN) to provide recommendations from the
previous history of similar users. Frequent itemsets,
a partitioning of user sessions into groups of similar
sessions, called session clusters or user profiles can
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also form a user model obtained using data mining. The three stages in User Modeling
Association rules can be discovered offline, and Stage-1: Collecting the Input Data
then used to provide recommendations based on The Two Paradigms: - Explicit vs Implicit
web navigation patterns. essentially, we have to have a model for collecting
Among the most popular methods, the ones feedback from the user on various items. In Explicit
based on collaborative filtering and the ones based User Modeling, the user is explicitly asked to rate
on fixed support association rule discovery may be his likeness for various items. The system records
the most difficult and expensive to use. This is the ratings given by the users and analyze then to
because, for the case of high-dimensional and deduce future likeness for new items. In Implicit
extremely sparse Web data, it is difficult to set User Modeling, the system automatically gathers
suitable support and confidence thresholds to yield information about an user's interests and needs.
reliable and complete web usage patterns. Similarly,
collaborative models may struggle with sparse data, Time relevance of data collected: - Often the user
and do not scale well to a very large number of is searching to quench an immediate information
users. thirst, which will typically last for a short time span,
Several methods for extracting keywords as soon as this information need is done with, the
that characterize web content have been proposed. user will generally be not interested in such
The similarity between documents is usually based information any more. To maximize the benefit for
on exact matching between these terms. The need the user in such cases through implicit user
for a more abstract representation that will enable a modeling, propose an eager implicit feedback. That
uniform and more flexible document matching is, as soon as any new evidence has been collected,
process imposes the use of semantic web structures, the systems belief on the information need about the
such as ontologies. By mapping the keywords to the user is updated, and the system then responds with
concepts of an ontology, or topic hierarchy, the updated model. Such a model however, increases
problem of binary matching can be surpassed the real time computational requirements and may
through the use of the hierarchical relationships not be scalable.
and/or the semantic similarities among the ontology
terms, and therefore, the documents. Surfing History Data for user profiling may be
gathered from the surfing history and surfing
User profiling: In the Web domain, user profiling is behavior of the user. This includes time of visits,
the process of gathering information specific to each last visits, frequency of visits, number of outlinks
visitor, either explicitly or implicitly and followed, scrolling patterns, dwelling time, mouse
representation of the user within the system. It is the clicks, mouse focuses etc. 'Curious browsers' have
first challenge to a personalized search system. A been designed to collect such data automatically and
user profile includes demographic information about implicitly wile the user surfs the net. Human-
the user, their interests and even their behavior when Computer Interaction systems may be employed to
browsing a Web site. This information is exploited gather more of such data, like eye movements and
in order to customize the content and structure of a eye-focus. Snippets gathered from page title, text
Web site to the visitor's specific and individual contents also give valuable clues. Surfing history for
needs. an user may also include query history and output
URLs selected by the user in the past.
Semantic Web: "The Semantic Web is an extension
of the current web in which information is given a User Bookmarks The bookmarks of a user are a
well-defined meaning, better enabling computers form of explicit feedback by the user and are a very
and people to work in cooperation." accurate source for gathering feedback.
The users’ navigation in a web site is
typically content-driven. The users usually search Client Data The IP address of a user gives us the
for information or services concerning a particular first clue towards personalization. As soon as we
topic. Therefore, the underlying content semantics have geographic classification of the user, we can
should be a dominant factor in the process of web present him with a personalized page that might
personalization. SEWeP (standing for Semantic contain weather information, local news etc. Such is
Enhancement for Web Personalization), a web geographic personalization. Even access methods
personalization framework that integrates content (browser and OS used) could be used to weakly
semantics with the users’ navigational patterns, infer some personalization features.
using ontologies to represent both the content and
the usage of the web site. The whole process bridges External Client Side Data The data stored in a
the gap between Semantic Web and Web client's desktop could be used to infer
Personalization areas, to create a Semantic Web personalization goals. This data may comprise of
Personalization system. documents and emails in the harddrives, most
frequented softwares etc.
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Stage-2: Inferring Profiles User Modeling Component
Preprocessing the collected data The collected In personalized search systems the user
data has to be cleaned first. The system has to be modeling component can affect the search in three
able to identify the user and track sessions. Log-in distinct phases:
forms and cookies are simple enough to identify
user. Part of retrieval process: The ranking is a unified
process wherein user profiles are employed to score
Profiling Finally we attempt to build the profile. Web contents. This is computationally most
We can employ Machine Learning algorithms, demanding.
statistical analysis etc. Domain knowledge and
ontologies can be incorporated in user inferring re-ranking: user profiles take part in a second step,
profiles. Also, it is necessary to continuously update after evaluating the corpus ranked via non-
the user profiling. Some common models are personalized scores. This is a client side approach.
clustering text (clustering is assigning items to
groups), classification/decision trees, discovery of Query modification: User profiles affect the
association rules, temporal pattern discovery, submitted representation of the information needs,
probabilistic models. e.g., query, modifying or augmenting it.
Personalization as a separate ranking factor
The idea is to use the 'distance' from the
user profile to the output URL as a separate ranking
factor. Thus, a second stage reranking is done on the
top n results returned by the search engine. This
reranking may be done with the profile as a bag of
words as shown. They also show an innovative
separation of the "permanent profile" from the
current profile. The current profile more effectively
reflects the immediate goals of an user session.
Reranking may be done by exploiting the
ODP as shown by. The profiles are topics from
ODP, and reranking is done as per the conceptual
similarity amongst the topics in profile and topics
associated with each output URL. The misearch
Figure. Link between user preferences and search system was developed by Speretta and Gauch. User
space profiles are represented as weighted concept
hierarchies. The ODP (Open Directory Project) is
Approaches for inferring profiles may be chosen as the base hierarchy. GOOGLE was chosen
generalized into two categories. Offline approaches as the search engine. There is a wrapper built to
pre-process history information mining relationships anonymously monitor activities. Two types of data
between queries and documents visited by users. are collected for each user: the submitted queries for
Online approaches use these data as soon as they are which at least one result was visited, and the
available. Online approaches are more dynamic and snippets, i.e., titles and textual summaries, of the
updated. But offline approaches can employ more results selected by the user. A classifier trained on
complex algorithms because there is no pressure to the ODPs hierarchy, chooses the concepts most
meet urgent time restrictions. related to the collected information, assigning higher
weights to them. After a query is submitted to the
Stage-3: Profile Representation wrapper, the snippets are classified into the same
Categories The user profile may be conceptualized reference concept hierarchy.
as a mapping of queries to categories. The internet
directories may be used for a hierarchical Outline the Solution
categorization of the web. The ODP is the most In this project, we studied the problem of
popular directory, along with a search engine. As personalized search. While there is some existing
we shall see, a lot of systems have been related work, it is far from optimal. We attempt to
experimented with the ODP as a base reference. address the issue How to personalize more elegant
Bag Of Words, List Of URLs In bag-of-words manner.
model, the user profile is simply an unordered
collection of words. Such models can be built by
applying naive Bayes classifiers. However, such a
model is a very weak representation of the user. So
is the case with list of URLs model. Fitting in The
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General Approach: define our ontology as a hierarchy of topics, where
the topics are utilized for the classification and
categorization of Web pages. The hierarchical
relationship among the concepts is taken into
consideration for building the ontological user
profile as we update the annotations for existing
concepts using spreading activation method. It is
used to maintain the interest scores based on the
user’s ongoing behavior.
Figure. General
approach for this project
The major challenges for personalized
search are two fold. The first is, using ontology to
identify topics that might be of interest to a
specific web user and building appropriate user
profile. The second is how to utilize the user
profile to improve search accuracy. Another
important challenge is evaluation of experiments.
There are no standard and bench mark datasets
available on which experiments can be performed.
This makes comparison with earlier work in the
literature and replicating their results difficult.
There are also no standard metrics available to
effectively evaluate personalized search
algorithms. Commonly used metrics in Figure. Portion of Ontological user profile where
Information Retrieval systems are usually used. Interest
An important requirement for building
personalized web search is to build user profiles Scores are updated on Spreading Activation
that represent the users’ interests. There are two The first challenge is building the user
representations commonly used for user profiles. profile. Building the user profile itself is a huge
One is using frequently occurring words in user research area called construction of User Profiles.
documents. This creates large profiles where In this project, user context refers to the context of
profile terms have low precision and have past searches made by the user. Many kinds of
insufficient context to determine the user interests. context information can be potentially exploited.
The other is using a pre-existing ontology such as For example, explicit context, implicit context,
DMOZ. short term context, long term context etc. Explicit
context consists of information given by a user
Our User Modeling Approach explicitly like one or more documents explicitly
Reference ontology and Interest score annotation marked by a user to be relevant to him. Implicit
for the concept. Our approach models the user’s context refers to any context information naturally
profiles by reusing the knowledge available in the available while a user interacts with a retrieval
domain ontology that is why we named them system. For example, if a user clicks a result
ontology-user-profiles. Specifically, we propose a among the list of results given for a query, the
semantic model for each user that gives clicked result was probably found to be interesting
information about: Concepts in the The user to the user. While explicit context information is
context is represented using an ontological user more reliable than implicit context, it is often not
profile, which is an annotated instance of available because it requires extra effort from the
reference ontology. The purpose of using user. Due to this, implicit context information
Ontology is to identify topics that might be of became an interesting alternative.
interest to a specific Web user. Therefore, we
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Search Query OD
P
User
Search Engine Context
Semantic Knowledge
Search Domain Ontology
Results
Spreadin
g
Activatio
n
Algorith
m
Ontological User Profile
Concepts with Updated
Activation Values
Interest Score
Updating Alg.
Ontological User Profiles with
Updated
Interest Scores
Re-ranking
Alg.
Results
Figure. Diagrammatic overview of this paper.
The second major challenge identified is: re- few results because a user mostly sees only the top
ranking to improve search results. few (typically 10) results.
The main goal of improving search results is to Our approach of re-ranking is as follows:
show more relevant results to the user on the top We first retrieve results for a query from a major
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search engine and consider the top few results and parses through the ODP standard database xml to
then compute a score for each document from the identify the relevant categories and the
top few results based on the user profile of the corresponding URL matches. If there is a match of
corresponding user. the URL with the ODP content, the corresponding
Re-rank algorithm is utilized to re-rank ODP elements and XML structure are then
the search results based on the interest scores and reranked and stored as part of the Output which is
the semantic evidence in the user profile. A term the requisite User profile that we build. The data is
vector r is computed for each document r € R, stored as part of a Collections object within the
where R is the set of search results for a given Engine.
query. The term weights are obtained using the Consistently over several rounds of
tf.idf formula described earlier. To calculate the search query and the corresponding ODP searches
rank score for each document, first the similarity resulted in output user profiles of similar nature.
of the document and the query is computed using a The ODP database is not a comprehensive one
cosine similarity measure. Then, we compute the which would have enabled us to run through
similarity of the. document with each concept in multiple levels and search queries for better
the user profile to identify the best matching analysis and evaluation. The sample database was
concept. a rudimentary one having a small content for us to
Once the best matching concept is the search and parse.
identified, a rank score is assigned to the User Profiles that are built form a basis
document by multiplying the interest score for the for future search optimization based on the interest
concept, the similarity of the document to the levels of the user. The interface that we have built
query and the similarity of the specific concept to could be bettered through usage of JSP and
the query. If the interest score for the best Servlets. We built the whole interface as a Java
matching concept is greater than one, it is further command line based interface where higher user
boosted by a tuning parameter Once all documents interaction and manual work is necessary. This is
have been processed, Then, we re arrange the a limitation in terms of capturing the user clicks
documents in descending order of the score with and getting to the desired URL and search
respect to this new rank score. category.
For re-ranking the results, we followed a Parsing technology and the engine have
common set up. User query is posed to a web been built using the SAXParser that is available as
search engine (Google) and top few results part of the JDK standard library set. Also, a
matching the query are retrieved. These top results standard search engine such as google has been
are then reranked using the user profile1. used. We have also used the much acclaimed
We performed experiments on data Google Ajax search API for Java to actually
extracted from query logs collected over 3 months interface with Google site and obtain the results.
by a popular search engine. We performed our One limitation is the usage of the URL Connection
experiments on 17 users from the query log. The object which actually can have some security
first two months of data is used to learn a profile implication.
and the third month data is used for evaluation. Definite enhancement is envisioned in
Our experiments showed that our approaches terms of providing a better user interface,
outperformed the baseline with a wide margin. generation of the user profile with the application
Our study of the same data has lead to some of the Spreading activation algorithm.
interesting observations closely related to the
problem addressed in this project. This has also Conclusion and Future work
motivated us to see if clickthrough data can be This Project develops a novel technique
created in dynamic way by simulating behaviour for web search personalization, where short-term
of users searching a search engine. We developed context is taken into consideration, not only as
a basic system which performs the same. another source of preference, but as a complement
for long-standing user profiles, in order to aid in
Results and Discussion the selection of the context-relevant preferences
This Project focused on building the User that can produce more reliable and “in context”
profile based on the reranking and assembly of the results.
search results. User’s search query is first passed We have presented a framework for
to a search engine such as Google and the contextual information access using ontologies
resultant URL’s are stored as an initial reference and demonstrated that the semantic knowledge
results on the Users local machine. The search embedded in an ontology combined with long-
query along with the URL’s from the result is then term user profiles can be used to effectively tailor
passed as part of a request to another engine. This search results based on users' interests and
engine parses the ODP standard database. The preferences. The models and methods proposed in
engine takes the URL from the Google results, the project build upon a user profile representation
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based on ontological concepts, which are richer [7] P. A. Chirita, W. Nejdl, R. Paiu, and C.
and more precise than classic keyword or Kohlschutter. Using odp metadata to
taxonomy based personalize search. In Proceedings of the
In our future work, we plan to continue 28th Annual International ACM SIGIR
evaluating the stability and convergence properties Conference on Research and
of the ontological profiles as interest scores are Development in Information Retrieval,
updated over consecutive interactions with the SIGIR 2005, pages 178-185, Salvador,
system. Since we focus on implicit methods for Brazil, August 2005.
constructing the user profiles, the profiles need to [8] P. A. Chirita, D. Olmedilla, and W.
adapt over time. Our future work will involve Nejdl. Pros: A personalized ranking
designing experiments that will allow us to platform for web search. In Proceedings
monitor user profiles over time to ensure the of the 3rd International Conference on
incremental updates to the interest scores ac- Adaptive Hypermedia and Adaptive
curately reflect changes in user interests. Web-Based Systems, AH 2004,
Eindhoven, The Netherlands, August
LIST OF REFERENCES 2004.
[1] M. Aktas, M. Nacar, and F. Menczer. [9] S. Dumais, T. Joachims, K. Bharat, and
Using hyperlink features to personalize A. Weigend. Implicit measures of user
web search. In Advances in Web Mining interests and preferences. A CM SIGIR
and Web Usage Analysis, Proceedings of Forum, 37(2), 2003.
the 6th International Workshop on [10] S. Gauch, J. Chaffee, and A. Pretschner.
Knowledge Discovery from the Web, Ontology-based personalized search and
WebKDD 2004, Seattle, WA, August browsing. Web Intelligence and Agent
2004. Systems, 1(3-4), 2003.
[2] H. Alani, K. O'Hara, and N. Shadbolt. [11] T. R,. Gruber. Towards principles for the
Ontocopi: Methods and tools for design of ontologies used for knowledge
identifying communities of practice. In sharing. In Formal Ontology in
Proceedings of the IFIP 17th World Conceptual Analysis and Knowledge
Computer Congress - TCI 2 Stream on Representation, Deventer, The
Intelligent Information Processing, pages Netherlands, 1993.
225-236, Deventer, The Netherlands, The [12] H. Haav and T. Lubi. A survey of
Netherlands, 2002. concept-based information retrieval tools
[3] J. Allan, et al. Challenges in information on the web. In 5th East-European
retrieval and language modeling. A CM Conference, ADBIS 2001, pages 29-41,
SIGIR Forum, 37(l):31-47, 2003. Vilnius, Lithuania, September 2001.
[4] O.Boydell and B. Smyth. Capturing [13] T. H. Haveliwala. Topic-sensitive
community search expertise for pagerank. In Proceedings of the 11th
personalized web search using snippet- International World Wide Web
indexes. In Proceedings of the 15th ACM Conference, WWW 2002, Honolulu,
International Conference on Information Hawaii, May 2002.
and Knowledge Management, CIKM [14] G. Jeh and J. Widom. Scaling
2006, pages 277-286, Arlington, VA, personalized web search. In Proceedings
November 2006. of the 12th international conference on
[5] H. Chang, D. Cohn, and A. McCallum. World Wide Web, WWW 2003, pages
Learning to create customized authority 271-279, Budapest, Hungary, May 2003.
lists. In Proceedings of the 7th [15] R. Kraft, F. Maghoul, and C. C. Chang.
International Conference on Machine Y!q: contextual search at the point of
Learning, ICML 2000, pages 127-134, inspiration. In Proceedings of the 14th
San Francisco, CA, July 2000. ACM International Conference on
[6] P. Chirita, C. Firan, and W. Nejdl. Information and Knowledge
Summarizing local context to personalize Management, CIKM 2005, pages 816-
global web search. In Proceedings of the 823, Bremen, Germany, November 2005.
15th ACM International Conference on [16] S. Lawrence. Context in web search.
Information and Knowledge IEEE Data Engineering Bulletin,
Management, CIKM 2006, pages 287- 23(3):25-32, 2000.
296, Arlington, VA, November 2006. [17] F. Liu, C. Yu, and W. Meng. Personalized
web search for improving retrieval
effectiveness. IEEE Transactions on
Knowledge and Data Engineering,
16(1):28-40, 2004.
240 | P a g e
8. C. Srinvas / International Journal of Engineering Research and Applications (IJERA) ISSN:
2248-9622 www.ijera.com Vol. 2, Issue 6, November- December 2012, pp.234-241
[18] A. Micarelli and F. Sciarrone. Anatomy [28] A. Sieg, B. Mobasher, S. Lytinen, and R.
and empirical evaluation of an adaptive Burke. Using concept hierarchies to
web-based information filtering system. enhance user queries in web-based
User Modeling and User-Adapted information retrieval. In Proceedings of
Interaction, 14(2-3):159-200, 2004. the International Conference on Artificial
[19] S. Middleton, N. Shadbolt, and D. D. Intelligence and Applications, IASTED
R.oure. Capturing interest through 2004, Innsbruck, Austria, February 2004.
inference and visualization: Ontological [29] A. Singh and K. Nakata. Hierarchical
user profiling in recommender systems. classification of web search results using
In Proceedings of the International personalized ontologies. In Proceedings
Conference on Knowledge Capture, K- of the 3rd International Conference on
CAP 2003, pages 62-69, Sanibel Island, Universal Access in Human-Computer
Florida, October 2003. Interaction, HCI International 2005, Las
[20] M. Porter. An Algorithm for suffix Vegas, NV, July 2005.
stripping. Program. 14 (3); 130-137, [30] M. Speretta and S. Gauch. Personalized
1980. search based on user search histories. In
[21] F. Qiu and J. Cho. Automatic Proceedings of the 2005 IEEE/WIC/ACM
identification of user interest for International Conference on Web
personalized search. In Proceedings of the Intelligence, WI 2005, pages 622-628,
15th International World Wide Web Compigne, France, September 2005.
Conference, WWW 2006, pages 727-736, [31] A. Spink, H. Ozmutlu, S. Ozmutlu, and B.
Edinburgh, Scotland, May 2006. Jansen. U.S. versus european web
[22] D. Ravindran and S. Gauch. Exploting searching trends. ACM SIGIR, Forum,
hierarchical relationships in conceptual 15(2), 2002.
search. In Proceedings of the 13th [32] F. Tanudjaja and L. Mui. Persona: A
International Conference on Information contextualized and personalized web
and Knowledge Management, ACM search. In Proceedings of the 35th Annual
CIKM 2004, Washington DC, November Hawaii International Conference on
2004. System Sciences, HICSS 2002, page 67,
[23] C. R.ocha, D. Schwabe, and M. P. de Big Island, Hawaii, January 2002.
Aragao. A hybrid approach for searching [33] J. Teevan, S. Dumais, and E. Horvitz.
in the semantic web. In Proceedings of Personalizing search via automated
the 13th international conference on analysis of interests and activities. In
World Wide Web, WWW 2004, pages Proceedings of the 28th Annual
374-383, New York, NY, USA, 2004. International ACM SIGIR Conference on
[24] G. Salton and C. Buckley. On the use of Research and Development in
spreading activation methods in Information Retrieval, SIGIR, 2005,
automatic information. In Proceedings of pages 449-456, Salvador, Brazil, August
the llth annual international ACM 2005.
SIGIR conference on Research and [34] J. Trajkova and S. Gauch. Improving
Development in Information Retrieval, ontology-based user profiles. In
SIGIR 1988, pages 147-160, Grenoble, Proceedings of the Recherche
France, 1988. d'Information Assiste par Ordinateur,
[25] G. Salton and M. McGill. Introduction to RIAO 2004, pages 380-389, University of
Modern Information Retrieval. McGraw- Avignon (Vaucluse), France, April 2004.
Hill, New York, NY, 1983.
[26] B. Schilit and M. Theimer. Disseminating Authors
active map information to mobile hosts.
IEEE Network, 8(5):22-32, 1994.
[27] X. Shen, B. Tan, and C. Zhai. Ucair:
Capturing and exploiting context for
personalized search. In Proceedings of the C. Srinivas, M.Tech(CSE), MCA,
Information Retrieval in Context B.Sc(CSE) working as a Associate Professor &
Workshop, SIGIR IRiX 2005, Salvador, Head Department of Informstion Technology, Sree
Brazil, August 2005. Visvesvaraya Institute of Technology & Science
(SVITS), Chowderpally (V), Devarkadra (Mdl),
Mahabubnagar, A.P-India. Research area includes
Artificial Intelligence & Neural Networks, Network
Security, Web Mining,
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