most citedYou Are What You Bought: Generating Customer Personas for E-commerce Applications

5 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2026

DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding

Zhuowen Liang, Xiaotian Lin, Yuyu Luo +2

Multimodal long-document question answering requires models to seek sparse evidence across pages and modalities, understand heterogeneous document content, and reason over the coll…

cs.AI2026

Gradient-Aligned Pair Selection for Personalized Preference Optimization

Ruoming Jin, Xinyu Li, Hao Zhou +6

Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Direct Preference Optimization…

cs.CL2026

Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs

Zhuowen Liang, Xiaotian Lin, Zhengxuan Zhang +3

Large language models (LLMs) are widely applied to data analytics over documents, yet direct reasoning over long, noisy documents remains brittle and error-prone. Hence, we study d…

cs.DC2025

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout

Ji Liu, Beichen Ma, Qiaolin Yu +7

Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices. The data distributed…

cs.IR2025★ 5 cited

You Are What You Bought: Generating Customer Personas for E-commerce Applications

Yimin Shi, Yang Fei, Shiqi Zhang +2

In e-commerce, user representations are essential for various applications. Existing methods often use deep learning techniques to convert customer behaviors into implicit embeddin…