232 citations · 1.4k across the 22 of their papers we have counts for
22 papers
DiffMM: Multi-Modal Diffusion Model for Recommendation
Yangqin Jiang, Lianghao Xia, Wei Wei +3
The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled personalized recommender systems to incorporate multiple modalities (such as visual, textual, a…
Graph Augmentation for Recommendation
Qianru Zhang, Lianghao Xia, Xuheng Cai +3
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, ev…
PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning
Wei Wei, Jiabin Tang, Yangqin Jiang +2
Multimedia online platforms (e.g., Amazon, TikTok) have greatly benefited from the incorporation of multimedia (e.g., visual, textual, and acoustic) content into their personal rec…
DiffKG: Knowledge Graph Diffusion Model for Recommendation
Yangqin Jiang, Yuhao Yang, Lianghao Xia +1
Knowledge Graphs (KGs) have emerged as invaluable resources for enriching recommendation systems by providing a wealth of factual information and capturing semantic relationships a…
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
Zhonghang Li, Lianghao Xia, Yong Xu +1
In recent years, there has been a rapid development of spatio-temporal prediction techniques in response to the increasing demands of traffic management and travel planning. While…
Spatio-Temporal Meta Contrastive Learning
Jiabin Tang, Lianghao Xia, Jie Hu +1
Spatio-temporal prediction is crucial in numerous real-world applications, including traffic forecasting and crime prediction, which aim to improve public transportation and safety…