Psychology-informed Recommender Systems (Paperback)

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.

General

Imprint:	Now Publishers Inc
Country of origin:	United States
Series:	Foundations and Trends (R) in Information Retrieval
Release date:	July 2021
First published:	2021
Authors:	Elisabeth Lex • Dominik Kowald • Paul Seitlinger • Thi Ngoc Trang Tran • Alexander Felfernig • Markus Schedl
Dimensions:	234 x 156 x 11mm (L x W x T)
Format:	Paperback
Pages:	122
ISBN-13:	978-1-68083-844-2
Categories:	Books > Computing & IT > General theory of computing > General Promotions
LSN:	1-68083-844-X
Barcode:	9781680838442

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