Toward Human-Aligned Recommender Systems via Cognitive and Behavioral User Modeling

Abstract

Recommender systems research has extensively explored personalization through user modeling, often relying on static representations of human attributes such as personality traits (e.g., the OCEAN framework), under the assumption that individuals with similar traits behave consistently across contexts. However, cognitive science shows that human decision-making is dynamic, shaped by cognitive biases, contextual trade-offs, uncertainty, and situation-dependent reasoning that are not captured by these representations. As a result, existing recommendation pipelines and evaluation frameworks often fail to reflect how individuals actually make decisions, particularly where preferences are fluid and there is no single optimal choice. This dissertation investigates how recommender systems can be more closely aligned with individual decision-making styles through cognitive and behavioral user modeling grounded in cognitive science theory and behavioral signals. This work pursues four research directions: (1) identifying key decision-making attributes that explain differences in user behavior, (2) investigating how these attributes can be computationally represented using both classical AI approaches and LLM-based reasoning frameworks, (3) exploring whether cognitive attributes can be inferred from behavioral interaction data using the RetailRocket e-commerce dataset, and (4) evaluating whether cognitively informed user representations improve perceived alignment, usefulness, and satisfaction through human-centered user studies. Overall, this research bridges cognitive science and recommender systems by introducing cognitive attributes as a complementary layer of user representation, moving beyond static preference assumptions toward behaviorally grounded, human-centered recommendations.