Refining Contrastive Learning and Homography Relations for Multi-Modal
  Recommendation
Authors: name: Shouxing Ma, name: Yawen Zeng, name: Shiqing Wu, name: Guandong Xu

Multi-modal recommender system focuses on utilizing rich modal information (
i.e., images and textual descriptions) of items to improve recommendation
performance. The current methods have achieved remarkable success with the
powerful structure modeling capability of graph neural networks. However, these
methods are often hindered by sparse data in real-world scenarios. Although
contrastive learning and homography ( i.e., homogeneous graphs) are employed to
address the data sparsity challenge, existing methods still suffer two main
limitations: 1) Simple multi-modal feature contrasts fail to produce effective
representations, causing noisy modal-shared features and loss of valuable
information in modal-unique features; 2) The lack of exploration of the
homograph relations between user interests and item co-occurrence results in
incomplete mining of user-item interplay.
  To address the above limitations, we propose a novel framework for
REfining multi-modAl contRastive learning
and hoMography relations (REARM). Specifically, we complement
multi-modal contrastive learning by employing meta-network and orthogonal
constraint strategies, which filter out noise in modal-shared features and
retain recommendation-relevant information in modal-unique features. To mine
homogeneous relationships effectively, we integrate a newly constructed user
interest graph and an item co-occurrence graph with the existing user
co-occurrence and item semantic graphs for graph learning. The extensive
experiments on three real-world datasets demonstrate the superiority of REARM
to various state-of-the-art baselines. Our visualization further shows an
improvement made by REARM in distinguishing between modal-shared and
modal-unique features. Code is available
https://github.com/MrShouxingMa/REARM{here}.