RecGPT Technical Report
Chao Yi, Dian Chen, Gaoyang Guo, Jiakai Tang, Jian Wu, Jing Yu, Sunhao Dai, Wen Chen, Wenjun Yang, Yuning Jiang, Zhujin Gao, Bo Zheng, Chi Li, Dimin Wang, Dixuan Wang, Fan Li, Fan Zhang, Haibin Chen, Haozhuang Liu, Jialin Zhu, Jiamang Wang, Jiawei Wu
2025-08-01
Summary
This paper talks about RecGPT, a system that uses large language models to make recommendations smarter by better understanding what users really want.
What's the problem?
The problem is that many recommendation systems struggle to correctly figure out what users intend or prefer, which can lead to boring or irrelevant suggestions and lower satisfaction.
What's the solution?
RecGPT solves this by integrating large language models that analyze user behavior and language to capture true user intent. This helps the system recommend a wider variety of content that better matches what users are looking for, while also helping merchants and platforms do better business.
Why it matters?
This matters because better recommendations make online experiences more enjoyable for users, increase sales and engagement for merchants, and improve the overall performance of platforms that rely on recommendations.
Abstract
RecGPT integrates large language models into recommender systems to focus on user intent, improving content diversity and satisfaction while enhancing merchant and platform performance.