Probabilistic document-context based relevance feedback with limited relevance judgments

Authors:
H. C. Wu;R. W. P. Luk;K. F. Wong;K. L. Kwok
Affiliations:
The Hong Kong Polytechnic University;The Hong Kong Polytechnic University;The Chinese University of Hong Kong;Queen's College, City University of New York
Venue:
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Year:
2006

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Abstract

This paper presents our novel relevance feedback (RF) algorithm that uses the probabilistic document-context based retrieval model with limited relevance judgments for document re-ranking. Probabilities of the document-context based retrieval model are estimated from the top N (=20) documents in the initial retrieval. We use document-context based cosine similarity measure to find similar data for better probability estimation in order to reduce the data scarcity problem and the negative weighting problem. Our RF algorithm is promising because its mean average precision is statistically significantly better than the baseline using TREC-6 and TREC-7 data collections.