Mining Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation (Overall Winner Award, Mood-track Winner Award)

TitleMining Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation (Overall Winner Award, Mood-track Winner Award)
Publication TypeConference Paper
Year of Publication2010
AuthorsShi, Y, Larson, MA, Hanjalic, A
Refereed DesignationRefereed
Conference NameCAMRa2010: Proceedings of the RecSys '10 Challenge on Context-aware Movie Recommendation
Pagination34-40
PublisherACM
Conference LocationBarcelona, Spain
ISBN Number978-1-4503-0258-6
Abstract

Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. Recommendations should also usually strive to satisfy a specific purpose. Within the Moviepilot mood track of the context-aware movie recommendation challenge, we propose a novel movie similarity measure that is specific to the movie property demanded by the challenge, i.e., movie mood. Our measure is further exploited by a joint matrix factorization model for recommendation. We experimentally validate the effectiveness of the proposed algorithm in exploiting mood-specific movie similarity for the recommendation with respect to several evaluation metrics, demonstrating that it could outperform several state-of-the-art approaches. In particular, mood-specific movie similarity is demonstrated to be more beneficial than general mood-based movie similarity, for the purpose of mood-specific recommendation.

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