Psychology-informed Recommender Systems
By Elisabeth Lex, Graz University of Technology, Austria, elisabeth.lex@tugraz.at | Dominik Kowald, Know-Center GmbH, Austria, dkowald@know-center.at | Paul Seitlinger, Tallinn University, Estonia, pseiti@tlu.ee | Thi Ngoc Trang Tran, Graz University of Technology, Austria, ttrang@ist.tugraz.at | Alexander Felfernig, Graz University of Technology, Austria, alexander.felfernig@ist.tugraz.at | Markus Schedl, Johannes Kepler University Linz and Linz Institute of Technology, Austria, markus.schedl@jku.at
Abstract
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, which do not incorporate the underlying cognitive reasons for user behavior in the algorithms’ design. The aim of this survey is to present 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. We call such systems psychology-informed recommender systems. The survey identifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affectaware recommender systems. Moreover, for each category, we highlight domains, in which psychological theory plays a key role and is therefore considered in the recommendation process. As recommender systems are fundamental tools to support human decision making, we also discuss selected phenomena related to human decision making that impact the interaction between a user and a recommender. Besides, we discuss related work that investigates the evaluation of recommender systems from the user perspective and highlight user-centric evaluation frameworks. We discuss potential research tasks for future work at the end of this survey.
Psychology-informed Recommender Systems
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 affectaware 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.
