Refining Contrastive Learning and Homography Relations for Multi-Modal
  Recommendation
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}.
68a5a213c4b3ea17d2d2cf47
Shouxing Ma
claimed_verified
2025-08-20T19:13:28.696Z
68a59cc87de3c48922820701
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/RXoYPK8yALphay3LFXvy-.png
ShouxingMa
user
MrShouxingMa
68a5a213c4b3ea17d2d2cf48
Yawen Zeng
68a5a213c4b3ea17d2d2cf49
Shiqing Wu
68a5a213c4b3ea17d2d2cf4a
Guandong Xu
admin_assigned
2025-08-21T10:59:52.343Z
682ad4f2d7bad2f7d4b528f0
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/wI2ZMU1EBmkiM-EjVlM2q.png
dongxu
user
guandongxu
Refining Contrastive Learning and Homography Relations for Multi-Modal
  Recommendation
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}.
A novel framework, REARM, enhances multi-modal recommender systems by refining contrastive learning and homography relations, improving feature representation and user-item interaction mining.
graph neural networks contrastive learning homography meta-network orthogonal constraint user interest graph item co-occurrence graph graph learning