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• W2022204750 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." @default.
• W2022204750 created "2016-06-24" @default.
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• W2022204750 date "2006-01-01" @default.
• W2022204750 modified "2023-09-24" @default.
• W2022204750 title "Relevance feedback models for recommendation" @default.
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• W2022204750 doi "https://doi.org/10.3115/1610075.1610138" @default.
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