Tracking changes in user interests with a few relevance judgments

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) pseudorelevance 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. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Clustering, Information Filtering, Relevance Feedback. General Terms Algorithms, Management, Experimentation. Keywords User Interest Tracking, Persistence Assumption, Pseudo-relevance Feedback, Incremental Data Clustering.

• Type: Conference
• Year: 2003
• Where: CIKM
• Authors: Dwi H. Widyantoro, Thomas R. Ioerger, John Yen
• Keywords: CIKM 2003, Data Clustering, Relevance Judgments, User Interest