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EMNLP
2006

Relevance Feedback Models for Recommendation

13 years 10 months ago
Relevance Feedback Models for Recommendation
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-theart item-based CF method.
Masao Utiyama, Mikio Yamamoto
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where EMNLP
Authors Masao Utiyama, Mikio Yamamoto
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