From the 1 of 7 linked papers with an AI index.
7 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…
AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
Mingdai Yang, Nurendra Choudhary, Jiangshu Du +4
Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-ex…
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…
PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems
Mingdai Yang, Fan Yang, Yanhui Guo +6
User modeling in large e-commerce platforms aims to optimize user experiences by incorporating various customer activities. Traditional models targeting a single task often focus o…