483 citations · 1.1k across the 14 of their papers we have counts for
15 papers
AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets
Tianyu Fan, Yuhao Yang, Yangqin Jiang +3
Large Language Models (LLMs) have demonstrated remarkable potential as autonomous agents, approaching human-expert performance through advanced reasoning and tool orchestration. Ho…
Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems
Xingchen Zou, Yuhao Yang, Zheng Chen +4
We introduce Traffic-R1, a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC), developed via self-exploration and iterative reinforcement of L…
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…
Aria-UI: Visual Grounding for GUI Instructions
Yuhao Yang, Yue Wang, Dongxu Li +4
Digital agents for automating tasks across different platforms by directly manipulating the GUIs are increasingly important. For these agents, grounding from language instructions…
Space-aware Socioeconomic Indicator Inference with Heterogeneous Graphs
Xingchen Zou, Jiani Huang, Xixuan Hao +6
Regional socioeconomic indicators are critical across various domains, yet their acquisition can be costly. Inferring global socioeconomic indicators from a limited number of regio…
HiGPT: Heterogeneous Graph Language Model
Jiabin Tang, Yuhao Yang, Wei Wei +4
Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for…
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…
GraphGPT: Graph Instruction Tuning for Large Language Models
Jiabin Tang, Yuhao Yang, Wei Wei +5
Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised learning (…
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…
Debiased Contrastive Learning for Sequential Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +3
Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GN…
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +3
Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recomme…
Knowledge Graph Contrastive Learning for Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +1
Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fru…