Paper Info
Title
Combining usage and content in an online recommendation system for music in the Long Tail.
Abstract
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.
Year
DOI
Venue
2013
10.1007/s13735-012-0025-1
IJMIR
Keywords
Field
DocType
Music recommendation, Hybrid recommender system, Usage data, Tags, Audio features
Recommender system,World Wide Web,Musical,Computer science,Loyalty,Acceptance rate,Long tail,Online evaluation,Usage data,Multimedia,Web site
Journal
Volume
Issue
ISSN
2
1
2192-662X
Citations 
PageRank 
References 
15
0.69
17
Authors
7
Name
Order
Citations
PageRank
Marcos Aurélio Domingues19715.33
Fabien Gouyon21038.54
Alípio Jorge374973.03
José Paulo Leal416233.07
João Vinagre5578.56
Luís Lemos6181.10
Mohamed Sordo715211.63