A Preliminary Study of a Recommender System for the Million Songs Dataset Challenge

In this paper, the preliminary study we have conducted on the Million Songs Dataset (MSD) challenge is described. The task of the competition was to suggest a set of songs to a user given half of its listening history and complete listening history of other 1 million people. We focus on memory-based collaborative filtering approaches since they are able to deal with large datasets in an efficient and effective way. In particular, we investigated on i) defining suitable similarity functions, ii) studying the effect of the “locality” of the collaborative scoring function, that is, how many of the neirest neighboors (and how much) they influence the score computation, and iii) aggregating multiple ranking strategies to define the overall recommendation. Using this technique we won the MSD challenge which counted about 150 registered teams.