  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. Then, we use
the attention module to identify inherent connections between user-item
interaction data and multimodal data, evaluate the impact of multimodal data on
different interactions, and achieve early-step fusion of item features.
Furthermore, our model optimizes item representation through the
attention-guided multi-step fusion strategy and contrastive learning to improve
recommendation performance. The extensive experiments on three real-world
datasets show that our model has superior performance compared to the
state-of-the-art models.
