RecGPT-V3 技術報告

摘要

大型語言模型（LLMs）正在將推薦系統從對歷史行為中共現模式的比對，轉變為對驅動行為意圖的推理。RecGPT-V1 在淘寶上率先實踐此範式，以用戶理解為核心；RecGPT-V2 則透過協調式多智能體推理將其規模化；兩者均已部署於生產環境，並在使用者體驗與商業成效上持續帶來正面效益。然而，大規模運行 RecGPT 揭示了三大挑戰：(1) 無狀態行為建模——每次請求都需重新處理完整用戶歷史，浪費計算資源且捨棄先前分析結果；(2) 標籤至物品的資訊瓶頸——以自然語言標籤作為用戶理解與物品錨定之間的有損通道；(3) 低效率的顯式推理——冗長的思維鏈導致不可接受的延遲與計算開銷。我們提出 RecGPT-V3，一種具狀態的混合模態推薦系統，它同時運用自然語言進行開放世界知識推理，以及語義 ID（SIDs）進行具體物品錨定。記憶中樞維護結構化且持續演進的用戶記憶，將長期行為濃縮為精簡單元，使使用者建模計算量降低 55.8%。混合模態基礎模型允許 LLM 共同對文字標籤與 SIDs 進行推理，開闢一條通往物品空間的高頻寬通道。潛在意圖推理將冗長的推理過程內化為緊湊的可學習潛在標記，這些標記仍可解碼為可讀的解釋，使輸出標記成本降低 200 倍。在淘寶「猜你喜歡」資訊流中部署後，RecGPT-V3 在大規模線上 A/B 測試中持續取得成效：IPV 提升 1.28%、CTR 提升 1.00%、TC 提升 1.97%、GMV 提升 3.97%，同時將端到端服務資源消耗降低 52.4%。

English

Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecGPT-V1 pioneered this paradigm on Taobao by centering user understanding, and RecGPT-V2 scaled it via coordinated multi-agent reasoning; both are deployed in production with consistent gains in user experience and commercial outcomes. However, operating RecGPT at scale reveals three challenges: (1) stateless behavior modeling, where each request reprocesses full user history, wasting computation and discarding prior analysis; (2) a tag-to-item information bottleneck, where natural-language tags form a lossy channel between user understanding and item grounding; and (3) inefficient explicit reasoning, whose lengthy chain-of-thought incurs untenable latency and compute overhead. We present RecGPT-V3, a stateful, hybrid-modal recommender that reasons over natural language for open-world knowledge and Semantic IDs (SIDs) for concrete item grounding. A Memory Hub maintains structured, continually evolving user memory that distills long-horizon behavior into condensed units, cutting user-modeling computation by 55.8%. A Hybrid-modal Foundation Model allows the LLM jointly reason over text tags and SIDs, opening a high-bandwidth channel into the item space. Latent Intent Reasoning internalizes verbose rationales into compact learnable latent tokens that remain decodable into readable explanations, lowering output token cost by 200x. Deployed in Taobao's "Guess What You Like" feed, RecGPT-V3 achieves consistent gains in large-scale online A/B tests: IPV +1.28%, CTR +1.00%, TC +1.97%, GMV +3.97%, while cutting end-to-end serving resource consumption by 52.4%.