Community-based matrix factorization for scalable music recommendation on smartphones

Mobile karaoke has attracted more attention as a popular mobile entertainment and social network platform, where music recommendations are highly desired to improve its user experiences. Traditional music recommendation methods suffer from the data sparsity issue and usually ignore the social interactions among users. In this paper, we propose a novel parallel community-based matrix factorization method which exploits implicit user behavior data to model user preferences from both social level, via community detection, and individual level. Both offline evaluation on a real dataset from Changba and online traffic investigations show the effectiveness of our method.