483 citations · 4.2k across the 70 of their papers we have counts for
16 papers · 1 filter
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…
GraphAgent: Agentic Graph Language Assistant
Yuhao Yang, Jiabin Tang, Lianghao Xia +3
Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that in…
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…
EasyST: A Simple Framework for Spatio-Temporal Prediction
Jiabin Tang, Wei Wei, Lianghao Xia +1
Spatio-temporal prediction is a crucial research area in data-driven urban computing, with implications for transportation, public safety, and environmental monitoring. However, sc…
AnyGraph: Graph Foundation Model in the Wild
Lianghao Xia, Chao Huang
The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current appro…
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…
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
Zhonghang Li, Lianghao Xia, Yong Xu +1
The objective of traffic prediction is to accurately forecast and analyze the dynamics of transportation patterns, considering both space and time. However, the presence of distrib…
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…
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…
OpenGraph: Towards Open Graph Foundation Models
Lianghao Xia, Ben Kao, Chao Huang
Graph learning has become essential in various domains, including recommendation systems and social network analysis. Graph Neural Networks (GNNs) have emerged as promising techniq…
UrbanGPT: Spatio-Temporal Large Language Models
Zhonghang Li, Lianghao Xia, Jiabin Tang +5
Spatio-temporal prediction aims to forecast and gain insights into the ever-changing dynamics of urban environments across both time and space. Its purpose is to anticipate future…
GraphEdit: Large Language Models for Graph Structure Learning
Zirui Guo, Lianghao Xia, Yanhua Yu +4
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural…