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A unified music recommender system using listening habits and semantics of tags

In this paper, we propose a unified music recommender system using both listening habits and semantics of tags in a social music site. Most commercial music recommender systems recommend music items based on the number of plays, or explicit ratings of a song. However, these approaches have some difficulties in recommending new items with only a few ratings, or recommending items to new users with little information. To resolve the problem, UniTag ontology is developed, which defines the meaning and the weighted score of tags. User profiles are created by combining the score of tags and the number of plays, and a collaborative filtering algorithm is executed. For performance evaluation, precisions, recalls, and F-measures are measured using the listening habits-based recommendation, the tag score-based recommendation, and the unified recommendation, respectively. Our experiments show that the proposed approach outperforms the other two approaches in terms of all the evaluation metrics.