A Hybrid Music Recommendation Algorithm Based on Attention Mechanism

Weite Feng, Tong Li, Haiyang Yu, Zhen Yang

Abstract: Music recommendation systems based on deep learning have been actively explored using hybrid approaches. However, most of the models proposed by previous studies adopt coarse-grained embedding approaches (e.g., CNNs) to characterize audio features. Users’ fine-grained preferences for music content have not been effectively explored yet. In this work, we propose a hybrid music recommendation model based on attention mechanism, which integrates user’s historical behaviour records and audio content and can capture the user’s fine-grained preferences for music content due to the introduction of attention mechanism. We experimented with a subset of the last.fm-1b dataset (30,753 users, 10,000 songs, 1533,245 interactions). The experimental results show that our method outperforms baselines approaches.