Relevance feedback models for recommendation

Authors:
Masao Utiyama;Mikio Yamamoto
Affiliations:
National Institute of Information and Communications Technology, Hikari-dai, Soraku-gun, Kyoto, Japan;University of Tsukuba, Tennodai, Tsukuba, Japan
Venue:
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Year:
2006

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Abstract

We extended language modeling approaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our approach is based on the analogy between IR and CF, especially between CF and relevance feedback (RF). Both CF and RF exploit users' preference/relevance judgments to recommend items. We first introduce a multinomial model that combines CF and CBF in a language modeling framework. We then generalize the model to another multinomial model that approximates the Polya distribution. This generalized model outperforms the multinomial model by 3.4% for CBF and 17.4% for CF in recommending English Wikipedia articles. The performance of the generalized model for three different datasets was comparable to that of a state-of-the-art item-based CF method.