AgentWebRec: Compact Evidence Fusion over the Agent Web for Personalized Recommendation

2026-10-01

AI Summary

This study addresses the challenge of personalized recommendation in agent networks, where evidence is fragmented, semantically heterogeneous, and privacy-isolated. To this end, we propose a user-agent-oriented framework that reformulates recommendation as a budget-constrained problem of sequential evidence acquisition and fusion. Specifically, the framework leverages large language model agents to perform conditional neighbor querying and multi-source evidence integration, dynamically optimizing query targets and memory retention strategies to achieve progressive, localized evidence aggregation. Experimental results demonstrate that the proposed method consistently outperforms existing baselines across four datasets. Furthermore, ablation studies validate the complementary gains contributed by each evidence layer.

Abstract

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.

Innovation

• Agent Web
• Evidence Fusion
• Personalized Recommendation
• LLM-based Agents
• Distributed Evidence Acquisition