Novel models and ensemble techniques to discriminate favorite items from unrated ones for personalized music recommendation

Abstract The Track 2 problem in KDD-Cup 2011 (music recommendation) is to discriminate between music tracks highly rated by a given user from those which are overall highly rated, but not rated by the given user. The training dataset consists of not only user rating history, but also the taxonomic information of track, artist, album, and genre. This paper describes the solution of the National Taiwan University team which ranked first place in the competition. We exploited a diverse of models (neighborhood models, latent models, Bayesian Personalized Ranking models, and random-walk models) with local blending and global ensemble to achieve 97.45% in accuracy on the testing dataset