Affiliations: [a] Department of Information Management, Cheng Shiu University, Kaohsiung, Taiwan | [b] Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan | [c] Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
Correspondence:
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Corresponding author: Ja-Hwung Su, Department of Information Management, Cheng Shiu University, Kaohsiung, Taiwan. E-mail:bb0820@ms22.hinet.net
Abstract: Recently, music recommender systems have been proposed to help users obtain
the interested music. Traditional recommender systems making attempts to
discover users' musical preferences by ratings always suffer from problems
of rating diversity, rating sparsity and lack of ratings. These problems
result in unsatisfactory recommendation results. To deal with traditional
problems, in this paper, we propose a novel music recommender system, namely
Multi-modal Music Recommender system (MMR), which integrates social and
collaborative information to predict users' preferences. In this work, the
playcounts are transformed into collaborative information to cope with
problem of lack of rating information, while item tags and artist tags are
employed as social information to cope with problems of rating diversity and
rating sparsity. Through optimizing the integrated social-and-collaborative
information, the users' preferences can be inferred more accurately and
efficiently. The experimental results reveal that, three problems can be
alleviated significantly and our proposed method outperforms other
state-of-the-art recommender systems in terms of RMSE (Root Mean Square
Error) and NDCG (Normalized Discount Cumulative Gain).
Keywords: Music recommendation, collaborative filtering, social content, data engineering, nonnegative matrix factorization
