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.
Author: | Niels Bertram, Jürgen DunkelORCiDGND, Ramón Hermoso |
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URN: | urn:nbn:de:bsz:960-opus4-28727 |
DOI: | https://doi.org/10.25968/opus-2872 |
DOI original: | https://doi.org/10.1016/j.eswa.2023.120347 |
ISSN: | 1873-6793 |
Parent Title (English): | Expert Systems with Applications |
Document Type: | Article |
Language: | English |
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 |
Volume: | 229 |
Issue: | A |
Article Number: | 120347 |
Link to catalogue: | 1858504503 |
Institutes: | Fakultät IV - Wirtschaft und Informatik |
DDC classes: | 004 Informatik |
Licence (German): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |