activity
20242026
most citedTreatment Effect Estimation for User Interest Exploration on Recommender Systems

8 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2026

Position-Aware Drafting for Inference Acceleration in LLM-Based Generative List-Wise Recommendation

Jiaju Chen, Chongming Gao, Chenxiao Fan +4

Large language model (LLM)-based generative list-wise recommendation has advanced rapidly, but decoding remains sequential and thus latency-prone. To accelerate inference without c…

cs.HC2026

AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments

Jiaju Chen, Jinghua Piao, Xia Xu +4

A long-standing challenge in economics lies not in the lack of intuition, but in the difficulty of translating intuitive insights into verifiable research. To address this challeng…

cs.IR2025

Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer

Chongming Gao, Kexin Huang, Ziang Fei +6

Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction. C…

cs.IR2024

DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems

Jiaju Chen, Chongming Gao, Shuai Yuan +3

The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recomm…

cs.IR2024★ 8 cited

Treatment Effect Estimation for User Interest Exploration on Recommender Systems

Jiaju Chen, Wenjie Wang, Chongming Gao +3

Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many…