Mining Specific and General Features in Both Positive and Negative Relevance Feedback. QUT E-Discovery Lab at the TREC'09 Relevance Feedback Track

摘要

User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovery knowledge are two critical problems. In TREC 2009, we participated in the Relevance Feedback Track and experimented a model consisting of two innovative stages: one for subject-based query expansion to extract pseudo-relevance feedback; one for relevance feature discovery to find useful patterns and terms in relevance judgements to rank documents. In this paper, the detailed description of our model is given, as well as the related discussions for the experimental results.