Addressing Cold Start for Next-song Recommendation

Abstract

The cold start problem arises in various recommendation applications. In this paper, we propose a tensor factorization-based algorithm that exploits content features extracted from music audio to deal with the cold start problem for the emerging application next-song recommendation. Specifically, the new algorithm learns sequential behavior to predict the next song that a user would be interested in based on the last song the user just listened to. A unique characteristic of the algorithm is that it learns and updates the mapping between the audio feature space and the item latent space each time during the iterations of the factorization process. This way, the content features can be better exploited in forming the latent features for both users and items, leading to more effective solutions for cold-start recommendation. Evaluation on a large-scale music recommendation dataset shows that the recommendation result of the proposed algorithm exhibits not only higher accuracy but also better novelty and diversity, suggesting its applicability in helping a user explore new items in next-item recommendation. Our implementation is available at https://github.com/fearofchou/ALMM.