Combining usage and content in an online recommendation system for music in the Long Tail

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Marcos Aurélio Domingues (Autor) Outra; Fabien Gouyon (Autor) Outra; Alípio Mário Jorge (Autor) FCUP; José Paulo Leal (Autor) FCUP; João Vinagre (Autor) Outra; Luís Lemos (Autor) Outra; Mohamed Sordo (Autor) Outra

Revista

Título: International Journal of Multimedia Information Retrieval

Vol. 2

Páginas: 3-13

ISSN: 2192-6611

Editora: Springer Nature

Indexação

ISI Web of Knowledge

Scopus - 41 Citações

Outras Informações

ID Authenticus: P-008-KVQ

DOI: 10.1007/s13735-012-0025-1

Abstract (EN): Nowadays, a large number of people consume music from the web. Web sites and online services now typically contain millions of music tracks, which complicates search, retrieval, and discovery of music. Music recommender systems can address these issues by recommending relevant and novel music to a user based on personal musical tastes. In this paper, we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real-time on a commercial web site, specialized in content from the very Long Tail of music content. We compare it against two stand-alone recommender systems, the first system based on usage and the second one based on content data (namely, audio and textual tags). The results show that the proposed hybrid recommender shows advantages with respect to usage-based and content-based systems, namely, higher user absolute acceptance rate, higher user activity rate and higher user loyalty. © 2012, Springer-Verlag London.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11