10 papers
TradingMoE: Routing the Right Experts in Evolving Markets
Chang Zhou, Xingtong Yu, Minbin Huang +4
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can…
Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation
Jie Luo, Qi Jin, Xinming Zhang
Multi-modal Sequential Recommendation (SR) incorporates rich side information (e.g., textual and visual features) to enhance dynamic user preference modeling. However, existing fra…
Event-Aware Prompt Learning for Dynamic Graphs
Xingtong Yu, Ruijuan Liang, Renhe Jiang +4
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural net…
GraphReAct: Reasoning and Acting for Multi-step Graph Inference
Xingtong Yu, Zhongwei Kuai, Chang Zhou +6
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to gra…
Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights
Xingtong Yu, Shenghua Ye, Ruijuan Liang +4
Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in gr…
Time-Aware Adaptive Side Information Fusion for Sequential Recommendation
Jie Luo, Wenyu Zhang, Xinming Zhang +1
Incorporating item-side information, such as category and brand, into sequential recommendation is a well-established and effective approach for improving performance. However, des…