6 citations · 8 across the 8 of their papers we have counts for
8 papers
Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce
Shicheng Fan, Mingdai Yang, Duohao Wang +9
In vibe coding, people describe software in natural language and delegate implementation to AI agents. By analogy, vibe commerce allows people to express buying or selling goals in…
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
Mingdai Yang, Shicheng Fan, Kejing Yu +5
LLM agents increasingly act as autonomous merchants that write their own product listings, and under competitive pressure, they fabricate attributes to win sales. Even under instru…
Personalized Recommendation Tool Learning via Autonomous Language Agents
Mingdai Yang, Zhiwei Liu, Weizhi Zhang +3
Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-ba…
Generative Long-term User Interest Modeling for Click-Through Rate Prediction
Jiangli Shao, Kaifu Zheng, Hao Fang +5
Modeling long-term user interests with massive historical user behaviors enhances click-through rate (CTR) prediction performance in advertising and recommendation systems. Typical…
Automating Personalization: Prompt Optimization for Recommendation Reranking
Chen Wang, Mingdai Yang, Zhiwei Liu +4
Modern recommender systems increasingly leverage large language models (LLMs) for reranking to improve personalization. However, existing approaches face two key limitations: (1) h…
Training Large Recommendation Models via Graph-Language Token Alignment
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, trad…
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Yuwei Cao, Liangwei Yang, Zhiwei Liu +5
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address thi…
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic
Huaiyuan Yao, Longchao Da, Vishnu Nandam +4
The integration of autonomous vehicles into urban traffic has great potential to improve efficiency by reducing congestion and optimizing traffic flow systematically. In this paper…