AI 中文总结
针对现有生成式推荐模型未利用跨用户协同信号的问题，提出OMEGA框架，通过潜在上下文压缩、协同记忆库与目标感知检索等机制，在多数据集上显著优于现有先进模型。
AI 中文摘要
生成式推荐（Generative Recommendation, GR）通过将物品转移建模为序列到序列任务展现出巨大潜力。尽管GR取得成功，现有框架主要聚焦于在受限的内部参数空间内建模单个用户序列，未能显式利用跨用户的协同信号。为解决该问题，我们提出OMEGA，一个用于生成式推荐的协同记忆增强框架。OMEGA弥合了隐式参数知识与显式协同信号之间的差距。我们首先引入潜在上下文压缩方法，利用可学习查询令牌将序列用户行为提炼为紧凑表示，大幅降低存储开销。这些压缩表示被聚合为协同记忆库，作为全局行为模式的显式存储库。为确保精准的知识获取，我们设计了轻量且目标感知的检索机制，通过同时考虑序列级和目标级相似性识别相关记忆。此外，配备门控交叉注意力机制的上下文感知集成模块，用于自适应融合检索到的协同记忆与局部用户上下文，同时减轻噪声模式的干扰。在多个真实世界数据集上的实证评估表明，OMEGA显著优于现有先进GR模型，验证了外部记忆作为生成范式补充的潜力。
英文摘要
Generative Recommendation (GR) has exhibited great potential by modeling item transitions as a sequence-to-sequence task. Despite the success of GR, existing frameworks primarily focus on modeling individual user sequences within a constrained internal parametric space, failing to explicitly leverage cross-user collaborative signals. To address this issue, we propose \textbf{OMEGA}, a cOllaborative MEmory augmentation framework for Generative recommendAtion. OMEGA bridges the gap between implicit parametric knowledge and explicit collaborative signals. We first introduce a latent context compression method that utilizes learnable query tokens to distill sequential user behavior into compact representations, significantly reducing storage overhead. These compressed representations are aggregated into a collaborative memory bank, serving as an explicit repository of global behavioral patterns. To ensure precise knowledge acquisition, we design a lightweight and target-aware retrieval mechanism that identifies pertinent memories by considering both sequence-level and target-level similarities. Furthermore, a context-aware integration module, equipped with a gated cross-attention mechanism, is employed to adaptively fuse the retrieved collaborative memories with the local user context while mitigating the interference of noisy patterns. Empirical evaluations on multiple real-world datasets demonstrate that OMEGA significantly outperforms existing advanced GR models, validating the potential of external memory as a complement to the generative paradigm.
CommentsAccepted by KDD 2026 Research Track
