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Caihong Sun

2 accepted papers

2026

Bridging the Semantic Gap: Leveraging LLMs for Hierarchical Interest Evolution in Sequential Recommendation

IJCAI 2026

Accurate user behavior modeling is fundamental to the prediction of click-through rates (CTR) in industrial recommendation systems and online advertising. Traditional discriminative models, which rely on isolated ID features, struggle to capture the evolving nature of user intents across multiple ch

Cited by 0Scholar
2024

Can Large Language Models Mine Interpretable Financial Factors More Effectively? A Neural-Symbolic Factor Mining Agent Model

ACL 2024findings

Finding interpretable factors for stock returns is the most vital issue in the empirical asset pricing domain. As data-driven methods, existing factor mining models can be categorized into symbol-based and neural-based models. Symbol-based models are interpretable but inefficient, while neural-based…

Cited by 1SourcePDFScholar