Enhancing Music Recommendation with Social Media Content: an Attentive Multimodal Autoencoder Approach
Tiancheng Shen, Jia Jia, Yan Li, Hanjie Wang, Bo Chen
Abstract
Music recommendation methods predict users' music preference primarily based on historical ratings. Meanwhile, manifold personal factors of users are also important for the problem, and research efforts have been made to improve the recommendation performance with auxiliary user information. As an important indicator of users' personal traits and states, the numerous social media content (e.g., texts, images and short videos), however, is still hardly exploited. In this work, we systematically study the utilization of multimodal social media content for music recommendation. We define groups of both targeted handcrafted features and generic deep features for each modality, and further propose an Attentive Multimodal Autoencoder approach (AMAE) to learn cross-modal latent representations from the extracted features. Attention mechanism is also employed to integrate users' global and contextual music preference with alterable weights. Experiments demonstrate remarkable improvement of recommendation performance (+2.40% in Hit Ratio and +3.30% in NDCG), manifesting the effectiveness of our AMAE approach, as well as the significance of incorporating social media content data in music recommendation.