Paper 81 (Research track)

Linking open public datasets for multifaceted music discovery

Author(s): Alo Allik, Delia Fano Yela, Mark Sandler

Abstract: Music artist recommendation has conventionally embraced the idea of providing users simple lists of suggestions based on similarity. Recommendation systems typically strive for efficiency and accuracy relying most frequently on different variants of bipartite graph filtering, content-based information or a hybrid of both. A more engaging music discovery system built on the principle of multifaceted representation, on the other hand, would arguably benefit from recommendation diversity to provide a novel and enriched experience of exploration. Here we propose an alternative music artist recommendation technique that not only reveals connections between artists, but also the nature of these artist connections to enhance music discovery. For this purpose we have developed a graph-based approach for music artist representation. This involves linking together a number of open public music-related datasets using Semantic Web technologies and Linked Data principles. Different types of data, including music publishing metadata, biographical and socio-cultural information, content-based feature extraction, and crowd-sourced tags, can thereby be combined into an integrated artist similarity graph.

Keywords: linked data; graph theory; music similarity; music information retrieval; machine learning

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