AI 中文总结
针对现有智能体推荐系统用户智能体视角狭窄的问题，提出AgentCom个性化沟通技能框架，通过「为什么-是什么-如何-谁」技能库及两种机制提升推荐性能，在三类推荐器中均有效。
AI 中文摘要
智能体推荐系统越来越多地采用基于大语言模型的用户智能体（UserAgent），在提供推荐前通过模拟反馈评估候选项目。然而，现有的用户智能体通常基于有限的个人历史独立推理，可能导致视角狭窄：智能体从局部且不完整的视角评估候选项目，忽略相关偏好维度，进而产生不准确的判断。缓解该问题的自然方式是引入其他用户作为顾问智能体，其多样的历史提供互补证据，帮助目标用户重新考虑被忽略的偏好信号。但通用的用户-顾问沟通过程不足，因为不同的用户决策状态需要不同形式的外部建议。基于此，我们提出AgentCom，一种面向智能体推荐系统的个性化沟通技能框架。AgentCom将可复用的沟通技能组织为共享的「为什么-是什么-如何-谁」技能库：「为什么」识别需要沟通的决策缺陷，「是什么」指定信息任务，「如何」确定顾问交互协议，「谁」检索能够执行该协议的顾问。为使共享技能库在使用时个性化且随时间自适应，AgentCom引入两种互补机制：个性化技能路由和失败驱动的技能演化。个性化技能路由通过为每个用户和推荐场景依次选择合适的技能来构建沟通路径；失败驱动的技能演化从不成功的沟通案例中学习，并用可复用的技能丰富共享库，以应对之前未发现的沟通需求。实验表明，AgentCom在传统、社交和智能体推荐器中均持续提升推荐性能。
英文摘要
Agentic recommender systems increasingly employ large language model-based UserAgents to evaluate candidate items through simulated feedback before recommendations are delivered. However, existing UserAgents typically reason in isolation based on limited personal histories, which may lead to perspective narrowing: the agent evaluates candidates from a local and incomplete view, overlooks relevant preference facets, and consequently produces inaccurate judgments. A natural way to alleviate this problem is to introduce other users as advisor agents, whose diverse histories provide complementary evidence that helps the target user reconsider overlooked preference signals. Nevertheless, a generic user-advisor communication process is insufficient, as different user decision states require different forms of external advice. Based on this insight, we propose AgentCom, a personalized communication skill framework for agentic recommender systems. AgentCom organizes reusable communication skills into a shared why--what--how--who skill bank: why identifies the decision deficiency that necessitates communication, what specifies the information task, how determines the advisor interaction protocol, and who retrieves advisors capable of executing that protocol. To make the shared skill bank personalized at use time and adaptive over time, AgentCom introduces two complementary mechanisms: personalized skill routing and failure-driven skill evolution. Personalized skill routing constructs a communication path by sequentially selecting suitable skills for each user and recommendation context. Failure-driven skill evolution learns from unsuccessful communication cases and enriches the shared bank with reusable skills that address previously uncovered communication needs. Experiments show that AgentCom consistently improves recommendation performance across traditional, social, and agentic recommenders.
Comments11 pages, 4 figures
