发表机构
National University of Singapore; Meta AI（新加坡国立大学; Meta AI）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生成式推荐中因共享参数导致的进化冲突问题，提出基于稀疏键值记忆层的LION框架，通过隔离记忆、强化进化与可扩展应用实现异质行为模式的有效自进化。
AI 中文摘要
生成式推荐已成为个性化推荐中一种有前景的端到端范式。然而，用户偏好随时间持续演变，使得自进化成为生成式推荐系统的一项关键能力。现有的进化策略，如持续再训练和基于蒸馏的适应，直接使用流式交互更新共享模型参数。尽管如此，我们发现将这些策略直接应用于生成式推荐会引入一个关键问题，称为进化冲突。具体而言，来自不同用户的异质偏好转变在完全共享的自回归参数空间内进行优化，导致主导行为模式逐渐主导模型进化过程，而代表性不足的模式则日益被忽视。为解决此问题，我们提出了一种用于生成式推荐的自进化记忆范式，旨在实现跨异质行为模式的有效进化。我们进一步确定了有效自进化推荐系统的三个关键原则，包括隔离记忆、强化进化和可扩展应用。在这些原则的指导下，我们开发了LION，一个以稀疏键值记忆层为核心的简单而有效的框架。具体来说，LION引入稀疏记忆激活以隔离不同行为模式的进化，同时设计了一个整合损失以在持续适应过程中强化对代表性不足的偏好动态的学习。在多种真实世界数据集上的大量实验证明了LION在各种持续进化设置（如每周期评估、用户/物品组评估和进化收敛分析）下的有效性。代码已在此https URL发布。
英文摘要
Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation. However, user preferences continuously evolve over time, making self-evolving an essential capability for generative recommender systems. Existing evolving strategies, such as continual retraining and distillation-based adaptation, directly update the shared model parameters using streaming interactions. Nevertheless, we find that directly applying such strategies to generative recommendation introduces a critical issue, termed evolution conflict. Specifically, heterogeneous preference shifts from different users are optimized within a fully shared autoregressive parameter space, causing dominant behavioral patterns to progressively dominate the model evolution process while underrepresented patterns become increasingly overlooked. To address this issue, we propose a self-evolving memory paradigm for generative recommendation, aiming to enable effective evolution across heterogeneous behavioral patterns. We further identify three key principles for effective self-evolving recommendation systems, including isolated memorization, reinforced evolution, and scalable application. Guided by these principles, we develop LION, a simple yet effective framework centered on a sparse Key-Value memory layer. Specifically, LION introduces sparse memory activation to isolate the evolution of different behavioral patterns, while a consolidation loss is designed to reinforce the learning of underrepresented preference dynamics during continual adaptation. Extensive experiments on diverse real-world datasets demonstrate the effectiveness of LION under various continual evolution settings (e.g., per-period evaluation, user/item group evaluation, and evolution convergence analysis). The codes are released at https://github.com/JazyJiang/Self-Evolving-Memory-for-Generative-Recommendation.
CommentsAccepted to CIKM'26
