Psychology-informed Recommender Systems
Elisabeth Lex, Markus Schedl
Personalized recommender systems have become critical means to support human decision making in today’s online world. Most of today’s recommender systems are data-driven and exploit behavioral data to learn user models and predict user preferences. While such systems can produce useful recommendations, they do not incorporate the underlying psychological reasons for user behavior in the algorithms’ design. The aim of this tutorial is to present recent work in psychology-informed recommender systems that leverages psychological constructs and theories to model and predict user behavior and improve the recommendation process. In the tutorial, we will discuss three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affect-aware recommender systems. The tutorial will conclude with grand challenges and potential research tasks for future work.
