Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering

Zheyu Chen, Jinfeng Xu, Yutong Wei, Ziyue Peng,

Paper

• A critical problem in existing GCL-based models is the irrational allocation of feature attention. This problem limits the model’s ability to effectively leverage crucial features, resulting in suboptimal performance. To address this, we propose a Weighted Graph Contrastive Learning framework (WeightedGCL).

COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation

Jinfeng Xu, Zheyu Chen, Wei Wang, Xiping Hu, Sang-Wook Kim, Edith Ngai,

Paper

• Modality fusion and representation learning were considered as two independent processes in previous work. In this paper, we reveal that these two processes are complementary and can support each other. Specifically, powerful representation learning enhances modality fusion, while effective fusion improves representation quality. Stemming from these two processes, we introduce a COmposite grapH convolutional nEtwork with dual-stage fuSION for the multimodal recommendation, named COHESION.

Don’t Lose Yourself: Boosting Multimodal Recommendation via Reducing Node-neighbor Discrepancy in Graph Convolutional Network

Zheyu Chen, Jinfeng Xu, Haibo Hu,

Paper

• To address the over-smoothing problem, we propose a novel model that retains the personalized information of ego nodes during feature aggregation by Reducing Node-neighbor Discrepancy (RedN^nD). [Code]

MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation

Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Hewei Wang, Edith C-H Ngai,

Paper

• To this end, we propose a Multi-level sElf-supervised learNing for mulTimOdal Recommendation (MENTOR) method to address the label sparsity problem and the modality alignment problem. [Code]

AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation

Jinfeng Xu, Zheyu Chen, Jinze Li, Shuo Yang, Hewei Wang, Edith C. H. Ngai,

Paper

• Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. [Code]

Improving Consumer Experience With Pre-Purify Temporal-Decay Memory-Based Collaborative Filtering Recommendation for Graduate School Application

Jinfeng Xu, Zheyu Chen, Zixiao Ma, Jiyi Liu, Edith C. H. Ngai,

Paper

• The Internet is booming with information, and it has become especially difficult for consumers to sift through the information. Recommendation systems can effectively enhance the consumer experience.

FourierKAN-GCF: Fourier Kolmogorov-Arnold Network – An Effective and Efficient Feature Transformation for Graph Collaborative Filtering

Jinfeng Xu, Zheyu Chen, Jinze Li, Shuo Yang, Wei Wang, Xiping Hu, Edith C-H Ngai,

Paper

• Graph Collaborative Filtering (GCF) has achieved state-of-the-art performance for recommendation tasks.