Psychology-informed Recommender Systems

This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.

Personalized recommender systems have become indispensable in today’s online world. Most of today’s recommendation algorithms are data-driven and based on behavioral data. While such systems can produce useful recommendations, they are often uninterpretable, black-box models that do not incorporate the underlying cognitive reasons for user behavior in the algorithms’ design. This survey presents a thorough review of the state of the art of recommender systems that leverage psychological constructs and theories to model and predict user behavior and improve the recommendation process - so-called psychology-informed recommender systems. The survey identifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affect-aware recommender systems. For each category, the authors highlight domains in which psychological theory plays a key role. Further, they discuss selected decision-psychological phenomena that impact the interaction between a user and a recommender. They also focus on related work that investigates the evaluation of recommender systems from the user perspective and highlight user-centric evaluation frameworks, and potential research tasks for future work at the end of this survey.