Concept based Personalized Web Search

Existing personalized web search systems does not take into account relevant pages that go unvisited by the user which might be direct answers to the user's information need. In addition, pages in the result set though not directly relevant to the user's information need might provide a link to relevant pages. Such links can be identified only by performing semantic analysis. This paper is aimed towards identifying such relevant pages through semantic search path analysis and provides an effective personalized web search. This improves web search by providing content and individual based relation between the search query and its relevant web pages.

[1]  George A. Miller,et al.  WordNet: A Lexical Database for the English Language , 2002 .

[2]  Alexander Pretschner,et al.  Ontology based personalized search , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[3]  Wen-Chih Peng,et al.  Ranking Web Search Results from Personalized Perspective , 2006, The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06).

[4]  Deborah Kania,et al.  One-to-One Web Marketing: Build a Relationship Marketing Strategy One Customer at a Time with Cdrom , 2001 .

[5]  Steffen Staab,et al.  Handbook on Ontologies (International Handbooks on Information Systems) , 2004 .

[6]  T. V. Geetha,et al.  Architecture for effective personalised web search , 2009, Int. J. Comput. Appl. Technol..

[7]  T. V. Geetha,et al.  Personalized Web Search Using Enhanced Probabilistic User Conceptual Index , 2008 .

[8]  Eetu Mäkel̈a.  Survey of Semantic Search Research , 2005 .

[9]  Lora Aroyo,et al.  Semantic Web-based Adaptive Hypermedia , 2004, WWW Workshop on Application Design, Development and Implementation Issues in the Semantic Web.

[10]  Alexander Pretschner,et al.  Ontology-based personalized search and browsing , 2003, Web Intell. Agent Syst..

[11]  Thorsten Joachims,et al.  Accurately interpreting clickthrough data as implicit feedback , 2005, SIGIR '05.

[12]  Douglas W. Oard,et al.  Implicit Feedback for Recommender Systems , 1998 .

[13]  Sameer Pradhan,et al.  Evaluation Metrics , 2007 .

[14]  Stuart E. Middleton,et al.  Capturing knowledge of user preferences: ontologies in recommender systems , 2001, K-CAP '01.

[15]  Susan Gauch,et al.  Improving Ontology-Based User Profiles , 2004, RIAO.

[16]  V. Cross,et al.  Metrics for ontologies , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[17]  T. V. Geetha,et al.  Ranking and Evaluation of Automatically Constructed Semantic Search Paths , 2008 .

[18]  T. V. Geetha,et al.  Personalized Web Search Using a Modified User Conceptual Index Based on a Search Flow Graph , 2007, IICAI.

[19]  Zhe Yang,et al.  Evaluation Metrics for Ontology Complexity and Evolution Analysis , 2006, 2006 IEEE International Conference on e-Business Engineering (ICEBE'06).

[20]  T. V. Geetha,et al.  Personalized ontology for web search personalization , 2008, Bangalore Compute Conf..

[21]  Bracha Shapira,et al.  Study of Effectiveness of Implicit Indicators and Their Optimal Combination for Accurate Inference of Users Interests , 2006, J. Digit. Inf. Manag..

[22]  T. V. Geetha,et al.  Web Search Using Personalized User Conceptual Index , 2005, IICAI.

[23]  Steffen Staab,et al.  International Handbooks on Information Systems , 2013 .

[24]  C. Lee Giles,et al.  Accessibility of information on the web , 1999, Nature.

[25]  Dennis McLeod,et al.  Yoda: An Accurate and Scalable Web-Based Recommendation System , 2001, CoopIS.

[26]  Yi-Shin Chen,et al.  Web Information Personalization: Challenges and Approaches , 2003, DNIS.

[27]  M. McLaughlin,et al.  Understanding of User Behavior in Immersive Environments , 2001 .

[28]  Asunción Gómez-Pérez,et al.  Selection of Ontologies for the Semantic Web , 2003, ICWE.