Multi-graph Fusion Cross-model Contrastive Learning for Recommendation
Shengjun Ma, Yuhai Zhao, Fenglong Ma, Baoyin Liu, Zhengkui Wang, Wen Shan
Abstract
Knowledge Graph (KG)-supported Graph Neural Network models are becoming crucial in recommendation systems due to their ability to mitigate the data sparsity challenge. However, these models remain suboptimal because they overlook the representation differences between the inherent user-item Bipartite Graph (BG) and the external head-relation-tail KG, leading to semantic misalignment. Moreover, they indiscriminately incorporate various types of relations from the KG, which may introduce noise information into the model, ultimately degrading recommendation performance. To address these challenges, we propose an end-to-end model named Multi-graph Fusion Cross-model Contrastive Learning (MFCCL). To uncover users
BibTeX
@inproceedings{aaai2026_multigraphfusion,
title = {Multi-graph Fusion Cross-model Contrastive Learning for Recommendation},
author = {Shengjun Ma and Yuhai Zhao and Fenglong Ma and Baoyin Liu and Zhengkui Wang and Wen Shan},
booktitle = {AAAI 2026},
year = {2026}
}