From hits to niches? or how popular artists can bias music recommendation and discovery

TitleFrom hits to niches? or how popular artists can bias music recommendation and discovery
Publication TypeConference Paper
Year of Publication2008
Conference Name2nd Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition (ACM KDD)
AuthorsCelma, Ò., & Cano P.
Conference Start Date24/08/2008
Conference LocationLas Vegas, USA
Keywordscomplex network analysis, evaluation, long tail, popularity, recommender systems
AbstractThis paper presents some experiments to analyse the popularity effect in music recommendation. Popularity is measured in terms of total playcounts, and the Long Tail model is used in order to rank music artists. Furthermore, metrics derived from complex network analysis are used to detect the influence of the most popular artists in the network of similar artists.

The results from the experiments reveal that, as expected by its inherent social component, the collaborative filtering approach is prone to popularity bias. This has some consequences on the discovery ratio as well as in the navigation through the Long Tail. On the other hand, in both audio content-based and human expert-based approaches artists are linked independently of their popularity. This allows one to navigate from a mainstream artist to a Long Tail artist in just two or three clicks.
preprint/postprint documenthttp://mtg.upf.edu/files/publications/Celma-ACM-Netflix-KDD2008.pdf
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