MemWeaver: 一种从文本交互行为构建的分层记忆用于个性化生成
MemWeaver: A Hierarchical Memory from Textual Interactive Behaviors for Personalized Generation
AI总结：
MemWeaver通过构建分层记忆模型，利用文本交互行为捕捉用户兴趣的时间演变和语义关系，实现深度个性化内容生成。
摘要：
用户与互联网的互动主要形式正从利用隐含反馈信号，如浏览和点击，转向利用文本交互行为提供的丰富显式反馈。这种转变解锁了用户文本历史的丰富来源，为更深层次的个性化提供了深刻的机会。然而，现有方法只能提供浅层的个性化，因为它们将用户历史视为文本的扁平列表进行检索，未能建模反映用户兴趣动态性质的丰富时间性和语义结构。在本工作中，我们提出了MemWeaver，一个将用户整个文本历史编织成分层记忆的框架，以驱动深度个性化生成。我们记忆的核心创新在于其能够捕捉兴趣的时间演变和不同活动之间的语义关系。为此，MemWeaver构建了两个互补的记忆组件，两者都整合了时间和语义信息，但抽象层次不同：行为记忆捕捉具体的用户行为，认知记忆代表长期偏好。这种双组件记忆为用户提供了全面的表示，使大型语言模型（LLMs）能够推理具体行为和抽象化认知特征。这导致了与潜在偏好深度一致的内容生成。在Language Model Personalization（LaMP）基准的六个数据集上的实验验证了MemWeaver的有效性。我们的代码已公开。
英文摘要：
The primary form of user-internet engagement is shifting from leveraging implicit feedback signals, such as browsing and clicks, to harnessing the rich explicit feedback provided by textual interactive behaviors. This shift unlocks a rich source of user textual history, presenting a profound opportunity for a deeper form of personalization. However, prevailing approaches offer only a shallow form of personalization, as they treat user history as a flat list of texts for retrieval and fail to model the rich temporal and semantic structures reflecting dynamic nature of user interests. In this work, we propose \textbf{MemWeaver}, a framework that weaves the user's entire textual history into a hierarchical memory to power deeply personalized generation. The core innovation of our memory lies in its ability to capture both the temporal evolution of interests and the semantic relationships between different activities. To achieve this, MemWeaver builds two complementary memory components that both integrate temporal and semantic information, but at different levels of abstraction: behavioral memory, which captures specific user actions, and cognitive memory, which represents long-term preferences. This dual-component memory serves as a comprehensive representation of the user, allowing large language models (LLMs) to reason over both concrete behaviors and abstracted cognitive traits. This leads to content generation that is deeply aligned with their latent preferences. Experiments on the six datasets of the Language Model Personalization (LaMP) benchmark validate the efficacy of MemWeaver. Our code is available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
