Bayesian Mixed-Effects Models for Recommender Systems
نویسندگان
چکیده
We propose a Bayesian methodology for recommender systems that incorporates user ratings, user features, and item features in a single unified framework. In principle our approach should address the cold-start issue and can address both scalability issues as well as sparse ratings. However, our early experiments have shown mixed results.
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