Tracking changes in user interests with a few relevance judgments

ABSTRACT

Keeping track of changes in user interests from a document stream with a few relevance judgments is not an easy task. To tackle this problem, we propose a novel method that integrates (1) pseudo-relevance feedback mechanism, (2) assumption about the persistence of user interests and (3) incremental method for data clustering. This approach has been empirically evaluated using Reuters-21578 corpus in a setting for information filtering. The experiment results reveal that it significantly improves the performances of existing user-interest-tracking systems without requiring additional, actual relevance judgments.

REFERENCES

• 1: Peter L. Bartlett , Shai Ben-David , Sanjeev R. Kulkarni, Learning changing concepts by exploiting the structure of change, Proceedings of the ninth annual conference on Computational learning theory, p.131-139, June 28-July 01, 1996, Desenzano del Garda, Italy [doi>10.1145/238061.238080]
• 2: Anil K. Jain , Richard C. Dubes, Algorithms for clustering data, Prentice-Hall, Inc., Upper Saddle River, NJ, 1988
• 3: Dov M. Gabbay , C. J. Hogger , J. A. Robinson, Handbook of logic in artificial intelligence and logic programming (Vol. 4): epistemic and temporal reasoning, Oxford University Press, Oxford, 1995
• 4: Bernard J. Jansen , Amanda Spink , Tefko Saracevic, Real life, real users, and real needs: a study and analysis of user queries on the web, Information Processing and Management: an International Journal, v.36 n.2, p.207-227, Jan.1.2000 [doi>10.1016/S0306-4573(99)00056-4]
• 5: Klinkenberg, R. (1999) Learning Drifting Concepts with Partial User Feedback, Beitrage zum Treffen der GI-Fachgruppe 1.1.3 Maschinelles Lernen (FGML-99), Perner, Petra and Fink, Volkmar (ed.).
• 6: Mandar Mitra , Amit Singhal , Chris Buckley, Improving automatic query expansion, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.206-214, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.290995]
• 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: Learning user interest dynamics with a three-descriptor representation, Journal of the American Society for Information Science and Technology, v.52 n.3, p.212-225, Feb. 1, 2001 [doi>10.1002/1532-2890(2000)9999:9999<::AID-ASI1615>3.3.CO;2-F]
• 9: Dwi H. Widyantoro , Thomas R. Ioerger , John Yen, An Incremental Approach to Building a Cluster Hierarchy, Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), p.705, December 09-12, 2002

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering; Relevance feedback

General Terms:
Algorithms, Experimentation, Management

Keywords:
incremental data clustering, persistence assumption, pseudo-relevance feedback, user interest tracking