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I am all EARS: Using open data and knowledge graph embeddings for music recommendations

  • 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.

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Author:Niels Bertram, Jürgen DunkelORCiDGND, Ramón Hermoso
DOI original:https://doi.org/10.1016/j.eswa.2023.120347
Parent Title (English):Expert Systems with Applications
Document Type:Article
Year of Completion:2023
Publishing Institution:Hochschule Hannover
Release Date:2023/06/05
Tag:Explainability; Graph embeddings; Knowledge graphs; Music recommender; Recommender systems
GND Keyword:Streaming <Kommunikationstechnik>; Musik; Empfehlungssystem; Wissensgraph; Eingebettetes System
Article Number:120347
Link to catalogue:1858504503
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:004 Informatik
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International