NEWS

2nd place in UrbanCup 2025: Urban Life Simulation with Large Language Models Empowered Agents

The final round of UrbanCup 2025 was held in a hackathon format, where expert judges from the conference evaluated and ranked teams based on their final presentations and technical reports. Out of 16 finalist teams, Team Bacon—composed of Yingzhi He, Leheng Sheng, Yuxin Chen, Yan Sun, and Peisen Zheng—achieved 2nd place in the competition.

Three papers accepted by ACL 2025

2nd place in AgentSociety Challenge in WWW: Personalized Recommendation Agents with Self-Consistency

This work presents the 2nd place solution for the WWW’25 AgentSociety Challenge (Recommendation Track), which focuses on developing LLM-based agents for personalized recommendation. Our team, RecHackers, proposes an agent-voting system that leverages self-consistency across multiple LLM outputs to improve ranking accuracy. The approach follows a prompting-sampling-voting paradigm: we design prompts based on user and item features, sample multiple candidate rankings from LLMs, and aggregate them using voting mechanisms such as majority voting or Borda count. This simple yet effective method demonstrates strong performance across real-world recommendation scenarios and achieves the 2nd place in the competition.

• Personalized Recommendation Agents with Self-Consistency

One paper accepted by KDD 2025

One paper has been accepted by SIGKDD 2025. Congratulations to Yingzhi He and Xiaohao Liu!

• LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation

One paper accepted by SIGIR 2025

One paper accepted by SIGIR 2025. Congratulations to all Guoqing Hu!

• Alphafuse: Learn id embeddings for sequential recommendation in null space of language embeddings

Three papers accepted by ICLR 2025

Three papers accepted by ICLR 2025. Congratulations to Chenhang Cui, Leheng Sheng, and Liu Shuo!

• Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment
• Language Representations Can be What Recommenders Need
• Preference Diffusion for Recommendation