Relevance feedback models for recommendation
|Relevance feedback models for recommendation|
|Author(s)||Utiyama M., Yamamoto M.|
|Published in||COLING/ACL 2006 - EMNLP 2006: 2006 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Collaborative filtering, Content based filtering, Data sets, Extended languages, Generalized models, Item-based CF, Language modeling, Multinomial model, Relevance feedback, Wikipedia, Computational linguistics, Feedback, Information retrieval, Natural language processing systems)|
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Relevance feedback models for recommendation is a 2006 conference paper written in English by Utiyama M., Yamamoto M. and published in COLING/ACL 2006 - EMNLP 2006: 2006 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference.
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.
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