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.
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Chao Yi
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Dian Chen
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Gaoyang Guo
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Jiakai Tang
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Jian Wu
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Jing Yu
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Sunhao Dai
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Wen Chen
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Wenjun Yang
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Yuning Jiang
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Zhujin Gao
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Bo Zheng
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Chi Li
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Dimin Wang
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Dixuan Wang
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Fan Li
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Fan Zhang
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Haibin Chen
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Haozhuang Liu
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Jialin Zhu
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Jiamang Wang
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Jiawei Wu
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Jin Cui
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Ju Huang
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Kai Zhang
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Kan Liu
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Lang Tian
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Liang Rao
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Longbin Li
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Lulu Zhao
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Mao Zhang
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Na He
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Peiyang Wang
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Qiqi Huang
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Tao Luo
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Wenbo Su
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Xiaoxiao He
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Xin Tong
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Xu Chen
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Xunke Xi
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Yang Li
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Yaxuan Wu
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Yeqiu Yang
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Yi Hu
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Yinnan Song
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Yuchen Li
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Yujie Luo
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Yujin Yuan
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Yuliang Yan
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Zhengyang Wang
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Zhibo Xiao
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Zhixin Ma
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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