M3KGR: A momentum contrastive multi-modal knowledge graph learning framework for recommendation

Abstract

In recent years, there has been a discernible upswing in the utilization of knowledge graphs within recommender systems. This heightened interest in knowledge graphs stems from their ability to vividly illustrate relationships between items through augmented attribute representations. Scholars have endeavored to incorporate multi-modal data, encompassing texts and images, into knowledge graphs, albeit with restricted success. These efforts primarily hinge on image models for extracting visual representations and text models for extracting textual representations, prompting apprehensions regarding potential disparities between the two modalities. Moreover, the current approaches for computing node similarities using multi-modal information have not witnessed noteworthy progress or enhancements.Momentum Contrastive Multi-Modal Knowledge Graph Learning Framework for Recommendation (M3KGR) proposed in this paper aims to enhance the utilization of multi-modal data. Specifically, we leverage the existing CLIP model to simultaneously extract textual and visual representations of an entity, thus overcoming the challenge of integrating multi-modal information. Additionally, we introduce a multi-modal enhanced attention technique to further enhance the performance of the graph attention network. Furthermore, we introduce Momentum Contrast to mitigate the common issues of data sparsity and noise in recommender systems. Our comprehensive experiments conducted on three real datasets demonstrate that our model enhances the efficacy of multi-modal knowledge graphs in recommender systems by fully leveraging the potential of multi-modal data. The implementation of our model will be available at https://github.com/KlaineWei/M3KGR.