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
The paper proposes CARP, a reputation‑penalty mechanism that discourages LLM agents from fabricating product attributes without needing ground‑truth verification, and shows that co…
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…