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Dian Chen

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Sep 2026

RecGPT: A User Intent-Centric Next-Generation LLM-Powered Recommender System in Industrial Practice

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.

Jiakai Tang, Wen Chen, Dian Chen et al. · 0 citations
Preprint Aug 2026

DREAM Technical Report

This work presents DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them, supporting agentic meta-control as a viable paradigm for industrial recommendation.

Bin Zhang, Bo-Wen Zheng, Chao Yi et al. · 0 citations
Jul 2026

RecGPT-V3 Technical Report

RecGPT-V3 is presented, a stateful, hybrid-modal recommender that reasons over natural language for open-world knowledge and Semantic IDs (SIDs) for concrete item grounding and achieves consistent gains in large-scale online A/B tests.

Bo-Wen Zheng, Chao Yi, Dian Chen et al. · 1 citation

TokenPilot: Cache-Efficient Context Management for LLM Agents

TokenPilot is presented, a dual-granularity context management framework that reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems.

Buqiang Xu, Z. Xue, Dian Chen et al. · 1 citation

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