483 citations · 4.2k across the 70 of their papers we have counts for
47 papers · 1 filter
MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs
Jiani Huang, Xingchen Zou, Lianghao Xia +1
The application of Large Language Models (LLMs) in recommender systems faces key challenges in delivering deep personalization and intelligent reasoning, especially for interactive…
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…
Pre-training for Recommendation Unlearning
Guoxuan Chen, Lianghao Xia, Chao Huang
Modern recommender systems powered by Graph Neural Networks (GNNs) excel at modeling complex user-item interactions, yet increasingly face scenarios requiring selective forgetting…
LightGNN: Simple Graph Neural Network for Recommendation
Guoxuan Chen, Lianghao Xia, Chao Huang
Graph neural networks (GNNs) have demonstrated superior performance in collaborative recommendation through their ability to conduct high-order representation smoothing, effectivel…
MixRec: Heterogeneous Graph Collaborative Filtering
Lianghao Xia, Meiyan Xie, Yong Xu +1
For modern recommender systems, the use of low-dimensional latent representations to embed users and items based on their observed interactions has become commonplace. However, man…
RecLM: Recommendation Instruction Tuning
Yangqin Jiang, Yuhao Yang, Lianghao Xia +3
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Net…
LightRAG: Simple and Fast Retrieval-Augmented Generation
Zirui Guo, Lianghao Xia, Yanhua Yu +2
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant respon…
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…
RecDiff: Diffusion Model for Social Recommendation
Zongwei Li, Lianghao Xia, Chao Huang
Social recommendation has emerged as a powerful approach to enhance personalized recommendations by leveraging the social connections among users, such as following and friend rela…
SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation
Yuxi Liu, Lianghao Xia, Chao Huang
Sequential recommendation effectively addresses information overload by modeling users' temporal and sequential interaction patterns. To overcome the limitations of supervision sig…
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…
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…
GraphPro: Graph Pre-training and Prompt Learning for Recommendation
Yuhao Yang, Lianghao Xia, Da Luo +2
GNN-based recommenders have excelled in modeling intricate user-item interactions through multi-hop message passing. However, existing methods often overlook the dynamic nature of…
Multi-Relational Contrastive Learning for Recommendation
Wei Wei, Lianghao Xia, Chao Huang
Personalized recommender systems play a crucial role in capturing users' evolving preferences over time to provide accurate and effective recommendations on various online platform…
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…
Knowledge Graph Self-Supervised Rationalization for Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +1
In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge conne…
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…
Graph Masked Autoencoder for Sequential Recommendation
Yaowen Ye, Lianghao Xia, Chao Huang
While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dep…
Denoised Self-Augmented Learning for Social Recommendation
Tianle Wang, Lianghao Xia, Chao Huang
Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve us…
Adaptive Graph Contrastive Learning for Recommendation
Yangqin Jiang, Chao Huang, Lianghao Xia
Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is…
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…