Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering

Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Ziyue Peng, Zewei Liu, Hewei Wang, Jiayi Zhang, and Edith Ngai

Paper

• We propose Multi-DProxy, a novel multi-modal dynamic proxy learning framework that leverages cross-modal alignment through learnable textual proxies.

A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions

Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Wei Wang, Xiping Hu, Steven Hoi, Edith Ngai

Paper

• This survey serves as a comprehensive guide for researchers and practitioners in MRS field, providing insights into the current state of MRS technology and identifying areas for future research.

Enhancing Robustness and Generalization Capability for Multimodal Recommender Systems via Sharpness-Aware Minimization

Jinfeng Xu, Zheyu Chen, Jinze Li, Shuo Yang, Wei Wang, Xiping Hu, Raymond Chi-Wing Wong, Edith Ngai

Paper

• We propose a sharpness-aware minimization strategy focused on batch data (BSAM), which effectively enhances the robustness and generalization capability of multimodal recommender systems without requiring extensive hyper-parameter tuning. Furthermore, we introduce a mixed loss variant strategy (BSAM+), which accelerates convergence and achieves remarkable performance improvement.

Hypercomplex Prompt-aware Multimodal Recommendation

Zheyu Chen, Jinfeng Xu, Hewei Wang, Shuo Yang, Zitong Wan, Haibo Hu

Paper

• We propose HPMRec, a novel Hypercomplex Prompt-aware Multimodal Recommendation framework, which utilizes hypercomplex embeddings in the form of multi-components to enhance the representation diversity of multimodal features. [Code]

Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation

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

Paper

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

The Best is Yet to Come: Graph Convolution in the Testing Phase for Multimodal Recommendation

Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Edith Ngai

Paper

NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation [spotlight oral]

Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Hewei Wang, Wei Wang, Xiping Hu, Edith Ngai

Paper [Code]

MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets

Jinfeng Xu, Zheyu Chen, Jinze Li, Shuo Yang, Hewei Wang, Yijie Li, Mengran Li, Puzhen Wu, Edith Ngai

Paper [Code]

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). [Code]

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. [Code]

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 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 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 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.

Generative AI for Vulnerability Detection in 6G Wireless Networks: Advances, Case Study, and Future Directions

Shuo Yang, Xinran Zheng, Jinfeng Xu, Jinze Li, Danyang Song, Zheyu Chen, Edith Ngai

Paper

• We introduce a three-layer framework comprising the Technology Layer, Capability Layer, and Application Layer to systematically analyze the role of VAEs, GANs, LLMs, and GDMs in securing next-generation wireless ecosystems.