Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909604546347008 |
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| author | Innerebner, Kevin Kowald, Dominik Schedl, Markus Lex, Elisabeth |
| author_facet | Innerebner, Kevin Kowald, Dominik Schedl, Markus Lex, Elisabeth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05083 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R Innerebner, Kevin Kowald, Dominik Schedl, Markus Lex, Elisabeth Information Retrieval 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. |
| title | Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2505.05083 |