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A collaborative filtering method for music recommendation using playing coefficients for artists and users

Diego Sánchez-Moreno,A. González,María Dolores Muñoz Vicente,Vivian F. López Batista,M. García

Published 2016 in Expert systems with applications

ABSTRACT

Proposal of a collaborative filtering (CF) method for music recommendation.The method is based on user and artist characterization.Only playing information that can be implicitly obtained is needed.The proposal can be applied for both rating prediction and item recommendation.The method outperforms other CF approaches. The great quantity of music content available online has increased interest in music recommender systems. However, some important problems must be addressed in order to give reliable recommendations. Many approaches have been proposed to deal with cold-start and first-rater drawbacks; however, the problem of generating recommendations for gray-sheep users has been less studied. Most of the methods that address this problem are content-based, hence they require item information that is not always available. Another significant drawback is the difficulty in obtaining explicit feedback from users, necessary for inducing recommendation models, which causes the well-known sparsity problem. In this work, a recommendation method based on playing coefficients is proposed for addressing the above-mentioned shortcomings of recommender systems when little information is available. The results prove that this proposal outperforms other collaborative filtering methods, including those that make use of user attributes.

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