Tracking changes in user interests with a few relevance judgments

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2003, Pages 548-551

  Widyantoro, Dwi H ^a   Ioerger, Thomas R ^a   Yen, John ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Incremental Data Clustering; Persistence Assumption; Pseudo relevance Feedback; User Interest Tracking

Indexed keywords

DATA HANDLING; EVOLUTIONARY ALGORITHMS; FEEDBACK; INFORMATION RETRIEVAL; QUERY LANGUAGES; SEARCH ENGINES;

INCREMENTAL DATA CLUSTERING; PERSISTENCE ASSUMPTION; PSEUDO-RELEVANCE FEEDBACK; USER INTEREST TRACKING;

INFORMATION MANAGEMENT;

EID: 18744372436     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/956958.956970     Document Type: Conference Paper

References (9)

1. Bartlett, P.L., David, S.B. and Kulkarni, S.R. (1996) Learning Changing Concepts by Exploiting the Structure of Change, Computational Learning Theory, pp. 131-139.
2. Jain, A.K. and Dubes, R.C. (1988) Algorithm for Clustering Data. Prentice Hall.
3. Gabbay, D.M, Hogger, C. J. and Robinson, J.A. (1995) Handbook of Logic in AI and Logic Programming: V4. Epistemic and Temporal Reasoning.
4. Jansen, B. J., Spink, A. and Saracevic, T. (2000) Real Life, Real Users and Real Needs: a Study and Analysis of Users Queries on the Web. Information Processing & Management, 36(2): 207-227.
5. Klinkenberg, R. (1999) Learning Drifting Concepts with Partial User Feedback, Beiträge zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen (FGML-99), Perner, Petra and Fink, Volkmar (ed.).
6. st ACM SIGIR Conference, pp. 206-214.
7. Rocchio, J.J. (1971) Relevance Feedback in Information Retrieval. In G. Salton, The SMART Retrieval System: Experiments in Automatic Doc. Processing, pp. 313-323.
8. Widyantoro, D.H., Ioerger, T.R. and Yen, J. (2001) Learning User Interest Dynamics with a Three-Descriptor Representation. Journal of the American Society for Information Science, 52(3): 212-225.
9. nd IEEE International Conference on Data Mining, pp. 705-708.