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.
Citation
Kevin Innerebner,
Dominik Kowald,
Markus
Schedl,
Elisabeth Lex
Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R
Proceedings of the 33nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP), doi:10.1145/3708319.3734176, 2025.
BibTeX
@inproceedings{KevinInnerebner2025HybPers,
title = {Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R},
author = {Kevin Innerebner and Dominik Kowald and Schedl, Markus and Elisabeth Lex},
booktitle = {Proceedings of the 33nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP)},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3708319.3734176},
year = {2025}
}