215 citations · 725 across the 19 of their papers we have counts for
14 papers · 1 filter
RecGPT: A Foundation Model for Sequential Recommendation
Yangqin Jiang, Xubin Ren, Lianghao Xia +3
This work addresses a fundamental barrier in recommender systems: the inability to generalize across domains without extensive retraining. Traditional ID-based approaches fail enti…
VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
Xubin Ren, Lingrui Xu, Long Xia +3
Retrieval-Augmented Generation (RAG) has demonstrated remarkable success in enhancing Large Language Models (LLMs) through external knowledge integration, yet its application has p…
EasyRec: Simple yet Effective Language Models for Recommendation
Xubin Ren, Chao Huang
Deep neural networks have emerged as a powerful technique for learning representations from user-item interaction data in collaborative filtering (CF) for recommender systems. Howe…
XRec: Large Language Models for Explainable Recommendation
Qiyao Ma, Xubin Ren, Chao Huang
Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adop…
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…
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…
How Expressive are Graph Neural Networks in Recommendation?
Xuheng Cai, Lianghao Xia, Xubin Ren +1
Graph Neural Networks (GNNs) have demonstrated superior performance on various graph learning tasks, including recommendation, where they leverage user-item collaborative filtering…
SSLRec: A Self-Supervised Learning Framework for Recommendation
Xubin Ren, Lianghao Xia, Yuhao Yang +4
Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despit…
Graph Transformer for Recommendation
Chaoliu Li, Lianghao Xia, Xubin Ren +3
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We hi…
Disentangled Contrastive Collaborative Filtering
Xubin Ren, Lianghao Xia, Jiashu Zhao +2
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastiv…
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
Xuheng Cai, Chao Huang, Lianghao Xia +1
Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance…
Deep Deconfounded Content-based Tag Recommendation for UGC with Causal Intervention
Yaochen Zhu, Xubin Ren, Jing Yi +1
Traditional content-based tag recommender systems directly learn the association between user-generated content (UGC) and tags based on collected UGC-tag pairs. However, since a UG…
Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation
Jing Yi, Xubin Ren, Zhenzhong Chen
Recommending appropriate tags to items can facilitate content organization, retrieval, consumption and other applications, where hybrid tag recommender systems have been utilized t…