Author(s) Name:  Elisabeth Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, Alexander Felfernig, Markus Schedl
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.
Table of contents:
1. Introduction
2. Psychology-informed Recommendation Approaches
3. Recommender Systems and Human Decision Making
4. User-centric Recommender Systems Evaluation
5. Conclusion and Suggestions for Future Research
ISBN:  978-1-68083-844-2
Publisher:  now publishers
Year of Publication:  2021
Book Link:  Home Page Url