Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R

• URL: http://arxiv.org/abs/2505.05083v1
• Date: Thu, 08 May 2025 09:32:04 GMT
• Title: Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
• Authors: Kevin Innerebner, Dominik Kowald, Markus Schedl, Elisabeth Lex,
• Abstract summary: We propose a hybrid user modeling framework based on the cognitive architecture ACT-R.<n>We aim to provide more transparent recommendations, enable rule-based explanations, and facilitate the modeling of cognitive biases.
• Score: 9.73847865216389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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