Improving Music Recommendation Using Distributed Representation

Dongjing Wang, ShuiGuang Deng, Songguo Liu, Guandong Xu

Abstract: In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods.