RecGPT: A User Intent-Centric Next-Generation LLM-Powered Recommender System in Industrial Practice
TL;DR
RecGPT is a fully integrated, production-ready 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, and transforms log-fitting recommendation into an intent-centric process.
Abstract
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, most 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’ potential interests. As a result, it reinforces filter bubbles and long-tail phenomena, ultimately harming user experience and threatening the sustainability of the recommendation ecosystem. To address these challenges, we rethink the overall design paradigm of recommender systems and propose RecGPT, a fully integrated, production-ready 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 (e.g., CICD +5.53%, DT +4.35%), merchants and the platform gain greater exposure and conversions (e.g., CTR +5.65%, IPV +7.55%, DCAU +2.46%), and sustained long-term retention improvements (LT-30 +1.63%). These comprehensive improvement results across all stakeholders validate that our intent-centric design can foster a more sustainable and mutually beneficial ecosystem.