I am all EARS: Using open data and knowledge graph embeddings for music recommendations
Bertram, Niels ; Dunkel, Jürgen ; Hermoso, Ramón (Universidad de Zaragoza)
Resumen: 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.
Idioma: Inglés
DOI: 10.1016/j.eswa.2023.120347
Año: 2023
Publicado en: Expert Systems with Applications 229 (2023), 120347 [17 pp.]
ISSN: 0957-4174
Factor impacto JCR: 7.5 (2023)
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 24 / 197 = 0.122 (2023) - Q1 - T1
Categ. JCR: OPERATIONS RESEARCH & MANAGEMENT SCIENCE rank: 6 / 106 = 0.057 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 25 / 353 = 0.071 (2023) - Q1 - T1
Factor impacto CITESCORE: 13.8 - Computer Science Applications (Q1) - Artificial Intelligence (Q1) - Engineering (all) (Q1)
Factor impacto SCIMAGO: 1.875 - Artificial Intelligence (Q1) - Computer Science Applications (Q1) - Engineering (miscellaneous) (Q1)
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-20R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-23R
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.
Exportado de SIDERAL (2024-11-22-12:02:06)
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Idioma: Inglés
DOI: 10.1016/j.eswa.2023.120347
Año: 2023
Publicado en: Expert Systems with Applications 229 (2023), 120347 [17 pp.]
ISSN: 0957-4174
Factor impacto JCR: 7.5 (2023)
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 24 / 197 = 0.122 (2023) - Q1 - T1
Categ. JCR: OPERATIONS RESEARCH & MANAGEMENT SCIENCE rank: 6 / 106 = 0.057 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 25 / 353 = 0.071 (2023) - Q1 - T1
Factor impacto CITESCORE: 13.8 - Computer Science Applications (Q1) - Artificial Intelligence (Q1) - Engineering (all) (Q1)
Factor impacto SCIMAGO: 1.875 - Artificial Intelligence (Q1) - Computer Science Applications (Q1) - Engineering (miscellaneous) (Q1)
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-20R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-23R
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material. Exportado de SIDERAL (2024-11-22-12:02:06)
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Record created 2023-07-28, last modified 2024-11-25