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

TL;DR

This paper proposes a hybrid user modeling framework based on ACT-R that combines symbolic and sub-symbolic memory modules to create more transparent, cognitively plausible recommender systems with explainability.

Contribution

It introduces a novel integration of ACT-R's declarative and procedural memory modules for user modeling in recommender systems, enhancing transparency and cognitive plausibility.

Findings

Framework enables simulation of user memory retrieval and decision strategies

Supports rule-based explanations and modeling of cognitive biases

Lays groundwork for psychology-informed recommender systems

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.