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View all- Du GZhou LLü KDing H(2021)Deep multiple non-negative matrix factorization for multi-view clusteringIntelligent Data Analysis10.3233/IDA-19507525:2(339-357)Online publication date: 1-Jan-2021
Effective social content-based collaborative filtering for music recommendation
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).
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- Effective social content-based collaborative filtering for music recommendation
Index terms have been assigned to the content through auto-classification.
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Published: 01 January 2017
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View all- Du GZhou LLü KDing H(2021)Deep multiple non-negative matrix factorization for multi-view clusteringIntelligent Data Analysis10.3233/IDA-19507525:2(339-357)Online publication date: 1-Jan-2021