推荐系统换血不换脑：新知识不毁旧模型
推荐系统每隔一阵就要用新数据重新训练，但一重训，之前学到的用户习惯就可能被冲掉，像换了个失忆的店员。这篇把「学到的知识」和「当前任务」拆开存放：知识放在一个可随时刷新的模块里，任务模块只读知识、不写回去，于是新数据进来只更新知识，不干扰已经调好的推荐逻辑。在 Shopee 首页搜索的线上测试里，这套做法让每个用户的成交额涨了 1.75%，广告收入涨了 1.53%。它已经上线，不是论文里的概念。
📄 原文摘要(英文)
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed. To resolve them, we propose Knowledge-Geometry Decoupling (KGD). For what to learn, conventional next-token prediction treats adjacency as dependency and may encode spurious transitions across unrelated sessions. We introduce Behavioral Multi-Token Prediction (BMTP) to retain only collaboratively or semantically related future items as supervision, yielding cleaner and more transferable behavioral knowledge. For how to transfer, pretrained knowledge and task-specific geometry impose conflicting optimization demands on shared parameters. To handle it, KGD assigns them to separate parameter sets: a refreshable encoder owns behavioral knowledge, while a task learner reads contextualized encoder states through read-only cross-attention and writes task-specific geometry through Anchored Calibration Residual (ACR) orthogonal to the pretrained embedding. The decoupled ownership enables continual knowledge refresh without task-gradient interference or invalidating downstream adaptation. KGD improves over strong pretrain-transfer baselines by 4-12% on eight public benchmarks and sustains its advantage over a 90-day production stream where baselines show no gains. KGD has been fully deployed in Shopee. In a live A/B test on Shopee Homepage Search, it increases GMV per user by 1.75% and advertising revenue by 1.53%, demonstrating its high practical value. We provide the core implementation of KGD at https://github.com/FuCongResearchSquad/KGD4REC.