  Multimodal Recommendation focuses mainly on how to effectively integrate
behavior and multimodal information in the recommendation task. Previous works
suffer from two major issues. Firstly, the training process tightly couples the
behavior module and multimodal module by jointly optimizing them using the
sharing model parameters, which leads to suboptimal performance since behavior
signals and modality signals often provide opposite guidance for the parameters
updates. Secondly, previous approaches fail to take into account the
significant distribution differences between behavior and modality when they
attempt to fuse behavior and modality information. This resulted in a
misalignment between the representations of behavior and modality. To address
these challenges, in this paper, we propose a novel Dual Representation
learning framework for Multimodal Recommendation called DRepMRec, which
introduce separate dual lines for coupling problem and Behavior-Modal Alignment
(BMA) for misalignment problem. Specifically, DRepMRec leverages two
independent lines of representation learning to calculate behavior and modal
representations. After obtaining separate behavior and modal representations,
we design a Behavior-Modal Alignment Module (BMA) to align and fuse the dual
representations to solve the misalignment problem. Furthermore, we integrate
the BMA into other recommendation models, resulting in consistent performance
improvements. To ensure dual representations maintain their semantic
independence during alignment, we introduce Similarity-Supervised Signal (SSS)
for representation learning. We conduct extensive experiments on three public
datasets and our method achieves state-of-the-art (SOTA) results. The source
code will be available upon acceptance.
