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Reseach Article

Web Usage Mining: Personalization based on User Positive and Negative Preferences

Published on April 2014 by Ravi. D, G. M. Nasira
International Conference on Knowledge Collaboration in Engineering
Foundation of Computer Science USA
ICKCE - Number 1
April 2014
Authors: Ravi. D, G. M. Nasira
ccc51ba3-2e63-487c-a52e-ba293aa05991

Ravi. D, G. M. Nasira . Web Usage Mining: Personalization based on User Positive and Negative Preferences. International Conference on Knowledge Collaboration in Engineering. ICKCE, 1 (April 2014), 26-29.

@article{
author = { Ravi. D, G. M. Nasira },
title = { Web Usage Mining: Personalization based on User Positive and Negative Preferences },
journal = { International Conference on Knowledge Collaboration in Engineering },
issue_date = { April 2014 },
volume = { ICKCE },
number = { 1 },
month = { April },
year = { 2014 },
issn = 0975-8887,
pages = { 26-29 },
numpages = 4,
url = { /proceedings/ickce/number1/16143-1009/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Knowledge Collaboration in Engineering
%A Ravi. D
%A G. M. Nasira
%T Web Usage Mining: Personalization based on User Positive and Negative Preferences
%J International Conference on Knowledge Collaboration in Engineering
%@ 0975-8887
%V ICKCE
%N 1
%P 26-29
%D 2014
%I International Journal of Computer Applications
Abstract

Most commercial search engines give the same results for the same query, not considering the user's interest. User profiling is a fundamental component of any personalization application. Most existing user profiling strategies are based on object that users are interested in ( positive preferences), but not the objects that users dislike ( negative preferences). This paper focuses on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences

References
  1. Di JIANG, Kenneth LEUNG, Wilfred NG and Hao LI. Beyond Click Graph: Topic Modeling for Search Engine Query Log Analysis. International Conference on Database Systems for Advanced Applications DASFAA 2013.
  2. Da YAN, Zhou ZHAO and Wilfred NG. Monochromatic and Bichromatic Reverse Nearest Neighbor Queries on Land Surfaces. ACM Conference on Information and Knowledge Management. ACM CIKM 2012.
  3. Di JIANG, Kenneth LEUNG and Wilfred NG. Context-Aware Search Personalization with Concept Preference. ACM Conference on Information and Knowledge Management. ACM CIKM 2011.
  4. Leung, K. W. -T. , Lee, D. L. , and Lee, W. -C. , Personalized Web Search with Location Preferences, Proc. of IEEE ICDE Conference, Long Beach, USA, 2010.
  5. K. W. -T. Leung, W. Ng, and D. L. Lee, "Personalized Concept-Based Clustering of Search Engine Queries," IEEE Trans. Knowledge and Data Eng. , vol. 20, no. 11, pp. 1505-1518, Nov. 2008.
  6. Open Directory Project, http://www. dmoz. org/, 2009.
  7. 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
  8. E. Agichtein, E. Brill, and S. Dumais, "Improving Web Search Ranking by Incorporating User Behavior Information," Proc. ACM SIGIR, 2006.
  9. M. Speretta and S. Gauch, "Personalized Search Based on User Search Histories," Proc. IEEE/WIC/ACM Int'l Conf. Web Intelligence, 2005
  10. R. Baeza-yates, C. Hurtado, and M. Mendoza, "Query Recommendation Using Query Logs in Search Engines," Proc. Int'l Workshop Current Trends in Database Technology, pp. 588-596, 2004.
  11. Q. Tan, X. Chai, W. Ng, and D. Lee, "Applying Co-training to Click through Data for Search Engine Adaptation," Proc. Database Systems for Advanced Applications (DASFAA) Conf. , 2004
  12. T. Joachims, "Optimizing Search Engines Using Click through Data," Proc. ACM SIGKDD, 2002.
  13. 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.
Index Terms

Computer Science
Information Sciences

Keywords

User Profiling Personalization Support Positive Preference Negative Preference