Next-song recommendation with temporal dynamics

Music recommendation has become an important way to reduce users’ burden in discovering songs that meet their interest from a large-scale online music site. Compared with general behavior, user listening behavior has a very strong time dependence in that users frequently change their music interest in different sessions, where the concept of a “session” is that of a single user continuously listening songs over a period of time. However, most existing methods ignore temporal dynamics of both users and songs across sessions. In this paper, we analyze the temporal characters of a real music dataset from Last.fm and propose Time-based Markov Embedding (TME), a next-song recommendation model via Latent Markov Embedding, which boost the recommendation performance by leveraging temporal information. Specifically, we consider a scenario where user music interest is affected by long-term, short-term and session-term effects. By capturing temporal dynamics in the three effects, our model can track the change of user interest over time. We have conducted experiments on Last.fm dataset. Results demonstrate that with our time-based model, the recommendation accuracy is significantly improved compared to other state-of-the-art methods.