  Multimedia recommendation has received much attention in recent years. It
models user preferences based on both behavior information and item multimodal
information. Though current GCN-based methods achieve notable success, they
suffer from two limitations: (1) Modality noise contamination to the item
representations. Existing methods often mix modality features and behavior
features in a single view (e.g., user-item view) for propagation, the noise in
the modality features may be amplified and coupled with behavior features. In
the end, it leads to poor feature discriminability; (2) Incomplete user
preference modeling caused by equal treatment of modality features. Users often
exhibit distinct modality preferences when purchasing different items. Equally
fusing each modality feature ignores the relative importance among different
modalities, leading to the suboptimal user preference modeling. To tackle the
above issues, we propose a novel Multi-View Graph Convolutional Network for the
multimedia recommendation. Specifically, to avoid modality noise contamination,
the modality features are first purified with the aid of item behavior
information. Then, the purified modality features of items and behavior
features are enriched in separate views, including the user-item view and the
item-item view. In this way, the distinguishability of features is enhanced.
Meanwhile, a behavior-aware fuser is designed to comprehensively model user
preferences by adaptively learning the relative importance of different
modality features. Furthermore, we equip the fuser with a self-supervised
auxiliary task. This task is expected to maximize the mutual information
between the fused multimodal features and behavior features, so as to capture
complementary and supplementary preference information simultaneously.
Extensive experiments on three public datasets demonstrate the effectiveness of
our methods.
