CARA：认知自适应推荐智能体
CARA: Cognitive Adaptive Recommendation Agent
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- The Chinese University of Hong Kong（香港中文大学）
- Tongji University（同济大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对现有推荐方法未明确建模用户偏好转化为决策的局限，提出认知自适应推荐框架CARA，经亚马逊评论实验，其多数指标性能最优，较基线相对提升最高达10.15%。
中文摘要 AI 辅助
大型语言模型和基于智能体的推荐框架的最新进展，为实现更灵活、更具上下文感知性的推荐带来了新机遇。然而，现有方法仍在很大程度上依赖语义匹配、端到端生成或结构松散的智能体工作流程，未明确建模用户偏好如何被处理并转化为最终决策。为解决这一局限，我们提出CARA，一种受认知启发的推荐框架，将推荐建模为结构化决策过程。CARA的核心直觉是，用户决策由两种互补机制共同塑造：直觉情感偏好与审慎理性评估。据此，CARA将推荐组织为两个协同阶段：候选筛选，基于粗粒度偏好约束缩小搜索空间；双视角决策建模，通过情感与理性判断捕捉推荐决策。我们进一步引入边界感知KTO策略，优先选择模型偶尔能解决但并非始终能解决的指令，从而提升信息偏好信号的密度。在三个亚马逊评论领域开展的大量实验表明，CARA在多数评估指标上取得了最佳性能，较基线的相对提升最高达10.15%。
英文摘要
Recent advances in large language models and agent-based recommendation frameworks have introduced new opportunities for more flexible and context-aware recommendation. However, existing methods still largely rely on semantic matching, end-to-end generation, or loosely structured agent workflows, without explicitly modeling how user preferences are processed and translated into final decisions. To address this limitation, we propose CARA, a cognitively inspired recommendation framework that formulates recommendation as a structured decision-making process. The core intuition of CARA is that user decisions are jointly shaped by two complementary mechanisms: intuitive affective preference and deliberate rational evaluation. Accordingly, CARA organizes recommendation into two coordinated stages: candidate filtering, which narrows the search space based on coarse-grained preference constraints, and dual-perspective decision modeling, which captures recommendation decisions through affective and rational judgment. We further introduce a boundary-aware KTO strategy that prioritizes instructions the model can solve occasionally but not consistently, thereby increasing the density of informative preference signals. Extensive experiments on three Amazon Reviews domains show that CARA achieves the best performance on most evaluation metrics, with relative improvements of up to 10.15% over the baseline.
