TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Bertram, Niels
A1  - Dunkel, Jürgen
A1  - Hermoso, Ramón
T1  - I am all EARS: Using open data and knowledge graph embeddings for music recommendations
JF  - Expert Systems with Applications
N2  - 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.
KW  - Music recommender
KW  - Knowledge graphs
KW  - Graph embeddings
KW  - Recommender systems
KW  - Explainability
KW  - Streaming <Kommunikationstechnik>
KW  - Musik
KW  - Empfehlungssystem
KW  - Wissensgraph
KW  - Eingebettetes System
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-28727
SN  - 1873-6793
SS  - 1873-6793
U6  - https://doi.org/10.25968/opus-2872
DO  - https://doi.org/10.25968/opus-2872
VL  - 229
IS  - A
ER  -