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Music recommendation using hypergraphs and group sparsity

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3 Author(s)
Theodoridis, A. ; Dept. of Inf., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece ; Kotropoulos, C. ; Panagakis, Y.

A challenging problem in multimedia recommendation is to model a variety of relations, such as social, friend, listening, or tagging ones in a unified framework and to exploit all these sources of information. In this paper, music recommendation problem is expressed as a hypergraph ranking problem, introducing group sparsity constraints. By doing so, one can control how the different data groups (i.e., sets of hypergraph vertices) affect the recommendation process. Experiments on a dataset collected from Last.fm demonstrate that the accuracy is significantly increased by exploiting the group structure of the data. Preliminary results are also presented for Greek folk music recommendation.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on

Date of Conference:

26-31 May 2013