CFP last date
15 December 2023
Call for Paper
January Edition
IJAIS solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 15 December 2023

Reseach Article

An Efficient Concept-based Mining Model for Deriving User Profiles

by P. Sasikala, V. Vidhya
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 6
Year of Publication: 2012
Authors: P. Sasikala, V. Vidhya

P. Sasikala, V. Vidhya . An Efficient Concept-based Mining Model for Deriving User Profiles. International Journal of Applied Information Systems. 1, 6 ( February 2012), 26-34. DOI=10.5120/ijais12-450187

@article{ 10.5120/ijais12-450187,
author = { P. Sasikala, V. Vidhya },
title = { An Efficient Concept-based Mining Model for Deriving User Profiles },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2012 },
volume = { 1 },
number = { 6 },
month = { February },
year = { 2012 },
issn = { 2249-0868 },
pages = { 26-34 },
numpages = {9},
url = { },
doi = { 10.5120/ijais12-450187 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-07-05T10:41:38.854957+05:30
%A P. Sasikala
%A V. Vidhya
%T An Efficient Concept-based Mining Model for Deriving User Profiles
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 6
%P 26-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

User profiling forms the basis for search engine personalization applications. Search engines are personalized so that they optimize the retrieval quality of user queries. User profiling done through concept-based mining identifies terms that render conceptual meaning as well as unimportant terms. Both positive and negative preferences from such conceptual terms are used in creating the user profiles and such profiles built based on both the preferences of a user reflect his/her interests at finer details. Based on these accurate and up-to-date user profiles, relationships between users can be mined to perform Collaborative Filtering (CF) thereby allowing users with the same interests to share their profiles. Collaborative filtering filters information about a user based on a collection of user profiles that are already built from the extracted preferences. Users having similar profiles may share similar interests. The concept-based search enhanced by Collaborative Filtering improves the relevancy of search results by making automatic predictions about the interests of a user by collecting similar user profiles.

  1. E. Agichtein, E. Brill, and S. Dumais, “Improving Web Search Ranking by Incorporating User Behavior Information,” Proc. ACM SIGIR, 2006.
  2. E. Agichtein, E. Brill, S. Dumais, and R. Ragno, “Learning User Interaction Models for Predicting Web Search Result Preferences,”Proc. ACM SIGIR, 2006.
  3. R. Baeza-yates, C. Hurtado, and M. Mendoza, “Query Recom-mendation Using Query Logs in Search Engines,” Proc. Int’l Workshop Current Trends in Database Technology, pp. 588-596, 2004.
  4. D. Beeferman and A. Berger, “Agglomerative Clustering of a Search Engine Query Log,” Proc. ACM SIGKDD, 2000.
  5. K.W. Church, W. Gale, P. Hanks, and D. Hindle, “Using Statistics in Lexical Analysis,” Lexical Acquisition: E xploiting On-Line Resources to Build a Lexicon, Lawrence Erlbaum, 1991.
  6. Z. Dou, R. Song, and J.R. Wen, “A Largescale Evaluation and Analysis of Personalized Search Strategies,” Proc. World Wide Web (WWW) Conf., 2007.
  7. S. Gauch, J. Chaffee, and A. Pretschner, “Ontology-Based Personalized Search and Browsing,” ACM Web Intelligence and Agent System, vol. 1, nos. 3/4, pp. 219-234, 2003.
  8. T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proc. ACM SIGKDD, 2002.
  9. K.W.T. Leung, W. Ng, and D.L. Lee, “Personalized Concept-Based Clustering of Search Engine Queries,” IEEE Trans. Knowl-edge and Data Eng., vol. 20, no. 11, pp. 1505-1518, Nov. 2008.
  10. B. Liu, W.S. Lee, P.S. Yu, and X. Li, “Partially Supervised Classification of Text Documents,” Pr o c. I nt ’ l C on f . M a ch in e Learning (ICML), 2002.
  11. F. Liu, C. Yu, and W. Meng, “Personalized Web Search by Mapping User Queries to Categories,” Proc. Int’l Conf. Information and Knowledge Management (CIKM), 2002.
  12. W. Ng, L. Deng, and D.L. Lee, “Mining User Preference Using Spy Voting for Search Engine Personalization,” ACM Trans. Internet Technology, vol. 7, no. 4, article 19, 2007.
  13. M. Speretta and S. Gauch, “Personalized Search Based on User Search Histories,” Proc. IEEE/WIC/ACM Int’l Conf. Web Intelligence,2005.
  14. Q. Tan, X. Chai, W. Ng, and D. Lee, “Applying Co-training to Clickthrough Data for Search Engine Adaptation,” Proc. Database Systems for Advanced Applications (DASFAA) Conf., 2004.
  15. J.R. Wen, J.Y. Nie, and H.J. Zhang, “Query Clustering Using User Logs,” ACM Trans. Information Systems, vol. 20, no. 1, pp. 59-81, 2002.
  16. Y. Xu, K. Wang, B. Zhang, and Z. Chen, “Privacy-Enhancing Personalized Web Search,” Proc. World Wide Web (WWW) Conf., 2007.
  17. K.Wai, T.Leung, D.L.Lee,”Deriving Concept-based user profiles from search engine logs” ,IEEE transactions on Knowledge and data engineering”, Vol.22, No.2,July 2010
Index Terms

Computer Science
Information Sciences


Clustering Collaborative Filtering Personalization Query formulation User profiles Personality diagnosis