AgentWebRec：面向个性化推荐的智能体网络紧凑证据融合

中文摘要 AI 辅助

基于大语言模型的个人智能体正逐渐成为用户语义的持久载体以及用户与推荐平台之间的中介，在本地维护更丰富的用户知识。随着智能体之间的相互交互，传统的“用户-平台”关系演变为“用户-智能体网络-平台”的信息通路，使得分布式的用户侧信息能够补充项目侧信息。然而，这一新通路挑战了传统推荐方式：证据分散在相互不透明的智能体中，且只能通过有限的查询获取，其中只有一小部分与当前的推荐决策相关，并且不同智能体返回的响应在语义上是异构的。因此，我们将智能体网络上的推荐重新定义为在有限证据预算下的“任务时证据获取与融合”问题，通过决定询问什么和保留什么来解决，而不是从聚合数据中学习。我们提出了AgentWebRec，一个面向用户-智能体的框架，该框架在保持底层智能体记忆本地化的同时，逐步获取并融合每个用户-项目决策的分布式证据。它将每个决策基于平台提供的项目语义和来自目标用户智能体私有记忆的任务相关证据，并在本地证据不足时，有条件地查询相邻用户智能体以获取互补的偏好模式。在四个InstructRec数据集上的实验表明，AgentWebRec始终优于基线推荐器，且消融实验验证了各证据层贡献了互补性的增益。

英文摘要

LLM-based personal agents are emerging as persistent carriers of user semantics and intermediaries between users and recommendation platforms, maintaining richer user knowledge locally. As agents interact with one another, the conventional \textit{User--Platform} relation evolves into a \textit{User--Agent Web--Platform} information pathway, enabling distributed user-side information to complement item-side information. This new pathway, however, defies conventional recommendation: evidence is scattered across mutually opaque agents and reachable only through bounded queries, only a small portion of it is relevant to the current recommendation decision, and the responses returned by different agents are semantically heterogeneous. We therefore recast recommendation over the agent web as a \emph{task-time evidence acquisition and fusion} problem under a finite evidence budget by deciding what to ask and what to keep, rather than learning from aggregated data. We propose AgentWebRec, a user-agent-oriented framework that progressively acquires and fuses distributed evidence for each user-item decision while keeping underlying agent memories local. It grounds each decision in platform-provided item semantics and task-relevant evidence from the target user agent's private memory, and conditionally queries neighboring user agents for complementary preference patterns when local evidence is insufficient. Experiments on four InstructRec datasets show that AgentWebRec consistently outperforms baseline recommenders, and ablations verify that the evidence layers contribute complementary gains.

发表机构

• Beihang University（北京航空航天大学）
• The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））

机构由 AI 辅助整理，请以论文原文为准。