RecGPT Technical Report
Recommender systems are among the most impactful applications of artificial
intelligence, serving as critical infrastructure connecting users, merchants,
and platforms. However, most current industrial systems remain heavily reliant
on historical co-occurrence patterns and log-fitting objectives, i.e.,
optimizing for past user interactions without explicitly modeling user intent.
This log-fitting approach often leads to overfitting to narrow historical
preferences, failing to capture users' evolving and latent interests. As a
result, it reinforces filter bubbles and long-tail phenomena, ultimately
harming user experience and threatening the sustainability of the whole
recommendation ecosystem.
  To address these challenges, we rethink the overall design paradigm of
recommender systems and propose RecGPT, a next-generation framework that places
user intent at the center of the recommendation pipeline. By integrating large
language models (LLMs) into key stages of user interest mining, item retrieval,
and explanation generation, RecGPT transforms log-fitting recommendation into
an intent-centric process. To effectively align general-purpose LLMs to the
above domain-specific recommendation tasks at scale, RecGPT incorporates a
multi-stage training paradigm, which integrates reasoning-enhanced
pre-alignment and self-training evolution, guided by a Human-LLM cooperative
judge system. Currently, RecGPT has been fully deployed on the Taobao App.
Online experiments demonstrate that RecGPT achieves consistent performance
gains across stakeholders: users benefit from increased content diversity and
satisfaction, merchants and the platform gain greater exposure and conversions.
These comprehensive improvement results across all stakeholders validates that
LLM-driven, intent-centric design can foster a more sustainable and mutually
beneficial recommendation ecosystem.
688aebf98b724c8c7187dd92
Chao Yi
688aebf98b724c8c7187dd93
Dian Chen
688aebf98b724c8c7187dd94
Gaoyang Guo
688aebf98b724c8c7187dd95
Jiakai Tang
claimed_verified
2025-08-06T19:32:11.944Z
65acfb3a14e6582c30b4ce76
https://cdn-avatars.huggingface.co/v1/production/uploads/65acfb3a14e6582c30b4ce76/RhEhePggBtyM0RIIqXQen.jpeg
TangJiakai
user
TangJiakai5704
688aebf98b724c8c7187dd96
Jian Wu
688aebf98b724c8c7187dd97
Jing Yu
688aebf98b724c8c7187dd98
Sunhao Dai
claimed_verified
2025-08-11T06:49:10.246Z
64db88993725f8d9a908c077
https://cdn-avatars.huggingface.co/v1/production/uploads/64db88993725f8d9a908c077/JZEGk0kius6mrANlwOWw9.jpeg
Sunhao Dai
user
KID-22
688aebf98b724c8c7187dd99
Wen Chen
688aebf98b724c8c7187dd9a
Wenjun Yang
688aebf98b724c8c7187dd9b
Yuning Jiang
688aebf98b724c8c7187dd9c
Zhujin Gao
688aebf98b724c8c7187dd9d
Bo Zheng
688aebf98b724c8c7187dd9e
Chi Li
688aebf98b724c8c7187dd9f
Dimin Wang
688aebf98b724c8c7187dda0
Dixuan Wang
688aebf98b724c8c7187dda1
Fan Li
688aebf98b724c8c7187dda2
Fan Zhang
688aebf98b724c8c7187dda3
Haibin Chen
688aebf98b724c8c7187dda4
Haozhuang Liu
688aebf98b724c8c7187dda5
Jialin Zhu
688aebf98b724c8c7187dda6
Jiamang Wang
688aebf98b724c8c7187dda7
Jiawei Wu
688aebf98b724c8c7187dda8
Jin Cui
688aebf98b724c8c7187dda9
Ju Huang
688aebf98b724c8c7187ddaa
Kai Zhang
688aebf98b724c8c7187ddab
Kan Liu
688aebf98b724c8c7187ddac
Lang Tian
688aebf98b724c8c7187ddad
Liang Rao
688aebf98b724c8c7187ddae
Longbin Li
688aebf98b724c8c7187ddaf
Lulu Zhao
688aebf98b724c8c7187ddb0
Mao Zhang
688aebf98b724c8c7187ddb1
Na He
688aebf98b724c8c7187ddb2
Peiyang Wang
688aebf98b724c8c7187ddb3
Qiqi Huang
688aebf98b724c8c7187ddb4
Tao Luo
688aebf98b724c8c7187ddb5
Wenbo Su
688aebf98b724c8c7187ddb6
Xiaoxiao He
688aebf98b724c8c7187ddb7
Xin Tong
688aebf98b724c8c7187ddb8
Xu Chen
688aebf98b724c8c7187ddb9
Xunke Xi
688aebf98b724c8c7187ddba
Yang Li
688aebf98b724c8c7187ddbb
Yaxuan Wu
688aebf98b724c8c7187ddbc
Yeqiu Yang
688aebf98b724c8c7187ddbd
Yi Hu
688aebf98b724c8c7187ddbe
Yinnan Song
688aebf98b724c8c7187ddbf
Yuchen Li
688aebf98b724c8c7187ddc0
Yujie Luo
688aebf98b724c8c7187ddc1
Yujin Yuan
688aebf98b724c8c7187ddc2
Yuliang Yan
688aebf98b724c8c7187ddc3
Zhengyang Wang
688aebf98b724c8c7187ddc4
Zhibo Xiao
688aebf98b724c8c7187ddc5
Zhixin Ma
688aebf98b724c8c7187ddc6
Zile Zhou
RecGPT Technical Report
Recommender systems are among the most impactful applications of artificial
intelligence, serving as critical infrastructure connecting users, merchants,
and platforms. However, most current industrial systems remain heavily reliant
on historical co-occurrence patterns and log-fitting objectives, i.e.,
optimizing for past user interactions without explicitly modeling user intent.
This log-fitting approach often leads to overfitting to narrow historical
preferences, failing to capture users' evolving and latent interests. As a
result, it reinforces filter bubbles and long-tail phenomena, ultimately
harming user experience and threatening the sustainability of the whole
recommendation ecosystem.
  To address these challenges, we rethink the overall design paradigm of
recommender systems and propose RecGPT, a next-generation framework that places
user intent at the center of the recommendation pipeline. By integrating large
language models (LLMs) into key stages of user interest mining, item retrieval,
and explanation generation, RecGPT transforms log-fitting recommendation into
an intent-centric process. To effectively align general-purpose LLMs to the
above domain-specific recommendation tasks at scale, RecGPT incorporates a
multi-stage training paradigm, which integrates reasoning-enhanced
pre-alignment and self-training evolution, guided by a Human-LLM cooperative
judge system. Currently, RecGPT has been fully deployed on the Taobao App.
Online experiments demonstrate that RecGPT achieves consistent performance
gains across stakeholders: users benefit from increased content diversity and
satisfaction, merchants and the platform gain greater exposure and conversions.
These comprehensive improvement results across all stakeholders validates that
LLM-driven, intent-centric design can foster a more sustainable and mutually
beneficial recommendation ecosystem.
RecGPT integrates large language models into recommender systems to focus on user intent, improving content diversity and satisfaction while enhancing merchant and platform performance.
large language models LLMs user intent user interest mining item retrieval explanation generation reasoning-enhanced pre-alignment self-training evolution Human-LLM cooperative judge system