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) 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.
pseudo-relevance feedback user interest tracking persistence assumption incremental data clustering