000126783 001__ 126783
000126783 005__ 20241125101139.0
000126783 0247_ $$2doi$$a10.1016/j.eswa.2023.120347
000126783 0248_ $$2sideral$$a134249
000126783 037__ $$aART-2023-134249
000126783 041__ $$aeng
000126783 100__ $$aBertram, Niels
000126783 245__ $$aI am all EARS: Using open data and knowledge graph embeddings for music recommendations
000126783 260__ $$c2023
000126783 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126783 5203_ $$aMusic 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.
000126783 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T64-20R$$9info:eu-repo/grantAgreement/ES/DGA/T64-23R
000126783 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000126783 590__ $$a7.5$$b2023
000126783 592__ $$a1.875$$b2023
000126783 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b24 / 197 = 0.122$$c2023$$dQ1$$eT1
000126783 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b6 / 106 = 0.057$$c2023$$dQ1$$eT1
000126783 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b25 / 353 = 0.071$$c2023$$dQ1$$eT1
000126783 593__ $$aArtificial Intelligence$$c2023$$dQ1
000126783 593__ $$aComputer Science Applications$$c2023$$dQ1
000126783 593__ $$aEngineering (miscellaneous)$$c2023$$dQ1
000126783 594__ $$a13.8$$b2023
000126783 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126783 700__ $$aDunkel, Jürgen
000126783 700__ $$0(orcid)0000-0002-1517-2820$$aHermoso, Ramón$$uUniversidad de Zaragoza
000126783 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000126783 773__ $$g229 (2023), 120347 [17 pp.]$$pExpert syst. appl.$$tExpert Systems with Applications$$x0957-4174
000126783 8564_ $$s4376162$$uhttps://zaguan.unizar.es/record/126783/files/texto_completo.pdf$$yVersión publicada
000126783 8564_ $$s2626483$$uhttps://zaguan.unizar.es/record/126783/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126783 909CO $$ooai:zaguan.unizar.es:126783$$particulos$$pdriver
000126783 951__ $$a2024-11-22-12:02:06
000126783 980__ $$aARTICLE