DRepMRec: A Dual Representation Learning Framework for Multimodal Recommendation

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.

An Aligning and Training Framework for Multimodal Recommendations

With the development of multimedia applications, multimodal recommendations are playing an essential role, as they can leverage rich contexts beyond user interactions. Existing methods mainly regard multimodal information as an auxiliary, using them to help learn ID features; however, there exist semantic gaps among multimodal content features and ID features, for which directly using multimodal information as an auxiliary would lead to misalignment in representations of users and items. In this paper, we first systematically investigate the misalignment issue in multimodal recommendations, and propose a solution named AlignRec. In AlignRec, the recommendation objective is decomposed into three alignments, namely alignment within contents, alignment between content and categorical ID, and alignment between users and items. Each alignment is characterized by a specific objective function and is integrated into our multimodal recommendation framework. To effectively train our AlignRec, we propose starting from pre-training the first alignment to obtain unified multimodal features and subsequently training the following two alignments together with these features as input. As it is essential to analyze whether each multimodal feature helps in training, we design three new classes of metrics to evaluate intermediate performance. Our extensive experiments on three real-world datasets consistently verify the superiority of AlignRec compared to nine baselines. We also find that the multimodal features generated by AlignRec are better than currently used ones, which are to be open-sourced.