用认知架构ACT-R构建可解释推荐系统，融合记忆与决策机制。
Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
- 结合ACT-R的陈述性记忆与程序性记忆，模拟用户经验回忆与决策。
- 支持规则化解释，可追踪推荐背后的认知逻辑。
- 适合心理学导向、需透明解释的推荐场景。
推荐系统多依赖黑箱式的子符号机器学习方法，难以体现影响用户偏好与决策的认知过程。本文提出一种基于认知架构ACT-R的混合用户建模范式，融合符号与子符号的人类记忆表示。通过整合ACT-R的陈述性记忆（存储符号块及子符号激活）与程序性记忆（包含符号生产规则），模拟用户如何检索过往经验并应用决策策略。该方法旨在提供更透明的推荐结果，支持规则化解释，并有助于建模认知偏差。我们相信该框架可为新一代以人为本、心理机制驱动的推荐系统设计提供启示。
原文摘要 · Abstract (English)
Recommender systems often rely on sub-symbolic machine learning approaches that operate as opaque black boxes. These approaches typically fail to account for the cognitive processes that shape user preferences and decision-making. In this vision paper, we propose a hybrid user modeling framework based on the cognitive architecture ACT-R that integrates symbolic and sub-symbolic representations of human memory. Our goal is to combine ACT-R's declarative memory, which is responsible for storing symbolic chunks along sub-symbolic activations, with its procedural memory, which contains symbolic production rules. This integration will help simulate how users retrieve past experiences and apply decision-making strategies. With this approach, we aim to provide more transparent recommendations, enable rule-based explanations, and facilitate the modeling of cognitive biases. We argue that our approach has the potential to inform the design of a new generation of human-centered, psychology-informed recommender systems.
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