{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:15:31Z","timestamp":1783541731806,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of recommender systems. In this work, we take advantage of the social network structure to improve recommendations for users with unusual preferences and new users, thus dealing with the gray-sheep and cold-start problems, respectively. Since collaborative filtering methods base the recommendations for a given user on the preferences of his\/her most similar users, the scarcity of users with similar tastes to the gray-sheep users and the unawareness of the preferences of the new users usually lead to bad recommendations. These general problems of recommender systems are worsened in the music domain, where the popularity bias drawback is also present. In order to address these problems, we propose a user similarity metric based on the network structure as well as on user ratings. This metric significantly improves the recommendation reliability in those scenarios by capturing both homophily effects in implicit communities of users in the network and user similarity in terms of preferences.<\/jats:p>","DOI":"10.3390\/info15030138","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T02:59:39Z","timestamp":1709175579000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems"],"prefix":"10.3390","volume":"15","author":[{"given":"Diego","family":"S\u00e1nchez-Moreno","sequence":"first","affiliation":[{"name":"Data Mining Research Group, University of Salamanca, 37008 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-7784","authenticated-orcid":false,"given":"Vivian F.","family":"L\u00f3pez Batista","sequence":"additional","affiliation":[{"name":"Data Mining Research Group, University of Salamanca, 37008 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4464-6888","authenticated-orcid":false,"given":"Mar\u00eda Dolores","family":"Mu\u00f1oz Vicente","sequence":"additional","affiliation":[{"name":"Data Mining Research Group, University of Salamanca, 37008 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1ngel Luis","family":"S\u00e1nchez L\u00e1zaro","sequence":"additional","affiliation":[{"name":"Data Mining Research Group, University of Salamanca, 37008 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-3707","authenticated-orcid":false,"given":"Mar\u00eda N.","family":"Moreno-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Data Mining Research Group, University of Salamanca, 37008 Salamanca, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"ref_1","unstructured":"Breese, J.S., Heckerman, D., and Kadie, C. 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