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

In this paper the preliminary study we are conducting on the Million Songs Dataset (MSD) challenge is described. The task of the competition is 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, and iii) aggregating multiple ranking strategies to define the overall recommendation. Using these techniques we are in the top positions according to the current standing of the competition leaderboard (at the moment of this writing the challenge has about 150 registered teams).