Enhancing Music Recommendation with Social Media Content: an Attentive Multimodal Autoencoder Approach

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...

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Vydáno v:Proceedings of ... International Joint Conference on Neural Networks s. 1 - 8
Hlavní autoři: Shen, Tiancheng, Jia, Jia, Li, Yan, Wang, Hanjie, Chen, Bo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2020
Témata:
Feature extraction
Image color analysis
Psychology
Recommender systems
Social network services
Videos
Visualization
ISSN:2161-4407
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Shrnutí: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.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9206894