The Community for Technology Leaders
RSS Icon
Issue No.07 - July (2010 vol.22)
pp: 969-982
Kenneth Wai-Ting Leung , Hong Kong University of Science and Technology, Hong Kong
Dik Lun Lee , Hong Kong University of Science and Technology, Hong Kong
User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e., positive preferences), but not the objects that users dislike (i.e., negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user's positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters.
Negative preferences, personalization, personalized query clustering, search engine, user profiling.
Kenneth Wai-Ting Leung, Dik Lun Lee, "Deriving Concept-Based User Profiles from Search Engine Logs", IEEE Transactions on Knowledge & Data Engineering, vol.22, no. 7, pp. 969-982, July 2010, doi:10.1109/TKDE.2009.144
[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] Appendix: 500 Test Queries, , 2009.
[4] 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.
[5] D. Beeferman and A. Berger, "Agglomerative Clustering of a Search Engine Query Log," Proc. ACM SIGKDD, 2000.
[6] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, "Learning to Rank Using Gradient Descent," Proc. Int'l Conf. Machine learning (ICML), 2005.
[7] K.W. Church, W. Gale, P. Hanks, and D. Hindle, "Using Statistics in Lexical Analysis," Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon, Lawrence Erlbaum, 1991.
[8] Z. Dou, R. Song, and J.-R. Wen, "A Largescale Evaluation and Analysis of Personalized Search Strategies," Proc. World Wide Web (WWW) Conf., 2007.
[9] 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.
[10] T. Joachims, "Optimizing Search Engines Using Clickthrough Data," Proc. ACM SIGKDD, 2002.
[11] 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.
[12] B. Liu, W.S. Lee, P.S. Yu, and X. Li, "Partially Supervised Classification of Text Documents," Proc. Int'l Conf. Machine Learning (ICML), 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.
[14] Magellan, http:/, 2008.
[15] 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.
[16] Open Directory Project, http:/, 2009.
[17] M. Speretta and S. Gauch, "Personalized Search Based on User Search Histories," Proc. IEEE/WIC/ACM Int'l Conf. Web Intelligence, 2005.
[18] 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.
[19] 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.
[20] Y. Xu, K. Wang, B. Zhang, and Z. Chen, "Privacy-Enhancing Personalized Web Search," Proc. World Wide Web (WWW) Conf., 2007.
3 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool