LoopMemGR：从行为日志到生成式推荐的演化记忆

中文摘要 AI 辅助

生成式推荐将下一个物品预测建模为基于离散语义ID的条件自回归生成，支持在大规模物品空间上的端到端推荐。然而，现有多数方法遵循“历史即上下文”范式，每次请求后会反复从行为历史中重构用户偏好，却丢弃了系统侧的推荐决策，形成了非对称记忆：系统记得用户的行为，却不记得之前的推荐或从反馈中学习到的内容，导致有用的偏好验证信号、潜在负例证据和历史探索信息无法在请求间直接复用。为解决这些局限，我们提出LoopMemGR，一种面向生成式推荐的闭环推荐体验记忆框架。除常规行为日志外，LoopMemGR还维护记录过往推荐-反馈轨迹的推荐体验日志，通过三个互补视图提取与请求相关的证据：近期视图捕捉短期交互动态，频率视图总结重复推荐模式，全局视图提炼用户间共享的可迁移规律。这些信号被压缩为固定数量的体验token，在有限输入预算下为生成主干提供条件。在工业级淘宝数据集上的大量实验验证了闭环体验积累和多视图体验提取的有效性。

英文摘要

Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs, enabling end-to-end recommendation over large-scale item spaces. However, most existing methods follow a history-as-context paradigm that repeatedly reconstructs user preference from behavior history while discarding system-side recommendation decisions after each request. This creates an asymmetric memory: the system remembers what the user has done, but not what it has previously recommended or learned from the resulting feedback. Consequently, useful preference-validation signals, potential negative evidence, and historical exploration information cannot be directly reused across requests. To address these limitations, we propose LoopMemGR, a closed-loop recommendation experience memory framework for generative recommendation. In addition to the conventional behavior log, LoopMemGR maintains a recommendation experience log that records past recommendation--feedback trajectories. It extracts request-relevant evidence through three complementary views: the recency view captures short-term interaction dynamics, the frequency view summarizes recurring recommendation patterns, and the global view distills transferable regularities shared across users. These signals are compressed into a fixed number of experience tokens to condition the generative backbone under a bounded input budget. Extensive experiments on an industrial Taobao dataset demonstrate the effectiveness of closed-loop experience accumulation and multi-view experience extraction.

发表机构

• Alibaba Group（阿里巴巴集团）
• Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）

机构由 AI 辅助整理，请以论文原文为准。