215 citations · 498 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
A Comprehensive Survey on Self-Supervised Learning for Recommendation
Xubin Ren, Wei Wei, Lianghao Xia +1
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep lear…
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…
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Wei Wei, Xubin Ren, Jiabin Tang +6
The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. Howev…
Representation Learning with Large Language Models for Recommendation
Xubin Ren, Wei Wei, Lianghao Xia +5
Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. How…
Automated Self-Supervised Learning for Recommendation
Lianghao Xia, Chao Huang, Chunzhen Huang +3
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contra…