126783 20241125101139.0 doi 10.1016/j.eswa.2023.120347 sideral 134249 ART-2023-134249 eng Bertram, Niels I am all EARS: Using open data and knowledge graph embeddings for music recommendations 2023 Access copy available to the general public Unrestricted Music streaming platforms offer music listeners an overwhelming choice of music. Therefore, users of streaming platforms need the support of music recommendation systems to find music that suits their personal taste. Currently, a new class of recommender systems based on knowledge graph embeddings promises to improve the quality of recommendations, in particular to provide diverse and novel recommendations. This paper investigates how knowledge graph embeddings can improve music recommendations. First, it is shown how a collaborative knowledge graph can be derived from open music data sources. Based on this knowledge graph, the music recommender system EARS (knowledge graph Embedding-based Artist Recommender System) is presented in detail, with particular emphasis on recommendation diversity and explainability. Finally, a comprehensive evaluation with real-world data is conducted, comparing of different embeddings and investigating the influence of different types of knowledge. info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00 info:eu-repo/grantAgreement/ES/DGA/T64-20R info:eu-repo/grantAgreement/ES/DGA/T64-23R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 7.5 2023 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 24 / 197 = 0.122 2023 Q1 T1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 6 / 106 = 0.057 2023 Q1 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 25 / 353 = 0.071 2023 Q1 T1 1.875 2023 Artificial Intelligence 2023 Q1 Computer Science Applications 2023 Q1 Engineering (miscellaneous) 2023 Q1 13.8 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Dunkel, Jürgen Hermoso, Ramón Universidad de Zaragoza (orcid)0000-0002-1517-2820 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 229 (2023), 120347 [17 pp.] Expert syst. appl. Expert Systems with Applications 0957-4174 4376162 http://zaguan.unizar.es/record/126783/files/texto_completo.pdf Versión publicada 2626483 http://zaguan.unizar.es/record/126783/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:126783 articulos driver 2024-11-22-12:02:06 ARTICLE