Exploiting Music Play Sequence for Music Recommendation

Exploiting Music Play Sequence for Music Recommendation

Zhiyong Cheng, Jialie Shen, Lei Zhu, Mohan Kankanhalli, Liqiang Nie

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3654-3660. https://doi.org/10.24963/ijcai.2017/511

Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recommender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal, we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the k-nearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance of both rating prediction and top-n recommendation tasks.
Keywords:
Multidisciplinary Topics and Applications: Personalization and User Modeling
Knowledge Representation, Reasoning, and Logic: Preference modelling and preference-based reasoning
Multidisciplinary Topics and Applications: Art and Music