Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation

Understand

The main idea of multimodal recommendation is the rational utilization of the item's multimodal information to improve the recommendation performance.

• Previous works directly integrate item multimodal features with item ID embeddings, ignoring the inherent semantic relations contained in the multimodal features.
• In this paper, we propose a novel and effective aTtention-guided Multi-step FUsion Network for multimodal recommendation, named TMFUN.
• Specifically, our model first constructs modality feature graph and item feature graph to model the latent item-item semantic structures.