2026-09-17T02:25:01Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1342492025-07-30T09:21:24Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243

Sánchez Moreno, Diego Gil González, Ana Belén Moreno García, María Navelonga López Batista, Vivian Félix Muñoz Vicente, María Dolores 2017-09-05T10:58:57Z 2017-09-05T10:58:57Z 2016 Expert Systems with Applications. Volumen 66, pp. 234-244. ELSEVIER. 0957-4174 http://hdl.handle.net/10366/134249 10.1016/j.eswa.2016.09.019 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. application/pdf en ELSEVIER Attribution-NonCommercial-NoDerivs 3.0 Unported https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Computer Science A collaborative filtering method for music recommendation using playing coefficients for artists and users. info:eu-repo/semantics/article