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Psychology-Informed Recommender Systems Full Text Available At Full text available at: http://dx.doi.org/10.1561/1500000090 Psychology-informed Recommender Systems Full text available at: http://dx.doi.org/10.1561/1500000090 Other titles in Foundations and Trends® in Information Retrieval Search and Discovery in Personal Email Collections Michael Bendersky, Xuanhui Wang, Marc Najork and Donald Metzler ISBN: 978-1-68083-838-1 Extracting, Mining and Predicting Users’ Interests from Social Media Fattane Zarrinkalam, Stefano Faralli, Guangyuan Piao and Ebrahim Bagheri ISBN: 978-1-68083-738-4 Knowledge Graphs: An Information Retrieval Perspective Ridho Reinanda, Edgar Meij and Maarten de Rijke ISBN: 978-1-68083-728-5 Deep Learning for Matching in Search and Recommendation Jun Xu, Xiangnan He and Hang Li ISBN: 978-1-68083-706-3 Explainable Recommendation: A Survey and New Perspectives Yongfeng Zhang and Xu Chen ISBN: 978-1-68083-658-5 Full text available at: http://dx.doi.org/10.1561/1500000090 Psychology-informed Recommender Systems Elisabeth Lex Graz University of Technology Dominik Kowald Know-Center GmbH Paul Seitlinger Tallinn University Thi Ngoc Trang Tran Graz University of Technology Alexander Felfernig Graz University of Technology Markus Schedl Johannes Kepler University Linz and Linz Institute of Technology Boston — Delft Full text available at: http://dx.doi.org/10.1561/1500000090 Foundations and Trends® in Information Retrieval Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 United States Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is A. Heezemans and M. Casey. Psychology-informed Recommender Systems. Founda- tions and Trends® in Information Retrieval, vol. 15, no. 2, pp. 134–242, 2021. ISBN: 978-1-68083-845-9 © 2021 A. Heezemans and M. Casey All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923. 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Please apply to now Publishers, PO Box 179, 2600 AD Delft, The Netherlands, www.nowpublishers.com; e-mail: [email protected] Full text available at: http://dx.doi.org/10.1561/1500000090 Foundations and Trends® in Information Retrieval Volume 15, Issue 2, 2021 Editorial Board Editors-in-Chief Maarten de Rijke Yiqun Liu Diane Kelly University of Amsterdam Tsinghua University University of Tennessee and Ahold Delhaize China USA The Netherlands Editors Barbara Poblete Lynda Tamine University of Chile University of Toulouse Claudia Hauff Mark Sanderson Delft University of Technology RMIT University Doug Oard Rodrygo Luis Teodoro Santos University of Maryland Universidade Federal de Minas Gerais Ellen M. Voorhees Ruihua Song National Institute of Standards and Renmin University of China Technology Ryen White Fabrizio Sebastiani Microsoft Research Consiglio Nazionale delle Ricerche, Italy Shane Culpepper Hang Li RMIT Bytedance Technology Soumen Chakrabarti Isabelle Moulinier Indian Institute of Technology Capital One Xuanjing Huang Jaap Kamps Fudan University University of Amsterdam Zi Helen Huang Jimmy Lin University of Queensland University of Waterloo Leif Azzopardi University of Glasgow Full text available at: http://dx.doi.org/10.1561/1500000090 Editorial Scope Topics Foundations and Trends® in Information Retrieval publishes survey and tutorial articles in the following topics: • Applications of IR • Metasearch, rank aggregation and data fusion • Architectures for IR • Collaborative filtering and • Natural language processing for recommender systems IR • Cross-lingual and multilingual • Performance issues for IR IR systems, including algorithms, data structures, optimization • Distributed IR and federated techniques, and scalability search • Question answering • Evaluation issues and test collections for IR • Summarization of single documents, multiple • Formal models and language documents, and corpora models for IR • Text mining • IR on mobile platforms • Topic detection and tracking • Indexing and retrieval of structured documents • Usability, interactivity, and • Information categorization and visualization issues in IR clustering • User modelling and user • Information extraction studies for IR • Information filtering and • Web search routing Information for Librarians Foundations and Trends® in Information Retrieval, 2021, Volume 15, 5 issues. ISSN paper version 1554-0669. ISSN online version 1554-0677. Also available as a combined paper and online subscription. Full text available at: http://dx.doi.org/10.1561/1500000090 Contents 1 Introduction3 1.1 Motivation.......................... 3 1.2 Main Approaches to Recommender Systems........ 5 1.3 Selected Recommender Systems Software and Datasets .. 6 1.4 Survey Method and Research Scope ............ 6 2 Psychology-informed Recommendation Approaches 18 2.1 Cognition-inspired Recommender Systems ......... 18 2.2 Personality-aware Recommender Systems .......... 31 2.3 Affect-aware Recommender Systems ............ 40 3 Recommender Systems and Human Decision Making 45 3.1 Decoy Items ......................... 47 3.2 Serial Position Effects .................... 48 3.3 Framing Effects ....................... 51 3.4 Anchoring Effects ...................... 51 3.5 Nudging ........................... 52 3.6 Discussion .......................... 54 4 User-centric Recommender Systems Evaluation 56 4.1 Psychological Aspects of User Experience ......... 57 Full text available at: http://dx.doi.org/10.1561/1500000090 4.2 Designing User Studies for Recommender Systems and Ex- isting Evaluation Frameworks ................ 63 4.3 Discussion .......................... 65 5 Conclusion and Suggestions for Future Research 68 References 73 Full text available at: http://dx.doi.org/10.1561/1500000090 Psychology-informed Recommender Systems Elisabeth Lex1, Dominik Kowald2, Paul Seitlinger3, Thi Ngoc Trang Tran1, Alexander Felfernig1 and Markus Schedl4 1Graz University of Technology; [email protected] 2Know-Center GmbH; [email protected] 3Tallinn University; [email protected] 1Graz University of Technology; [email protected] 1Graz University of Technology; [email protected] 4Johannes Kepler University Linz and Linz Institute of Technology; [email protected] ABSTRACT Personalized recommender systems have become indispens- able in today’s online world. Most of today’s recommenda- tion algorithms are data-driven and based on behavioral data. While such systems can produce useful recommen- dations, 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 con- structs and theories to model and predict user behavior and improve the recommendation process. We call such systems psychology-informed recommender systems. The survey iden- tifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affect- aware recommender systems. Moreover, for each category, Elisabeth Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, Alexan- der Felfernig and Markus Schedl (2021), “Psychology-informed Recommender Sys- tems”, Foundations and Trends® in Information Retrieval: Vol. 15, No. 2, pp 134–242. DOI: 10.1561/1500000090. Full text available at: http://dx.doi.org/10.1561/1500000090 2 we highlight domains, in which psychological theory plays a key role and is therefore considered in the recommen- dation 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 evalua- tion 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. Full text available at: http://dx.doi.org/10.1561/1500000090 1 Introduction 1.1 Motivation In the past twenty years, research on recommender systems has emerged as a growing field within computer science (Ricci et
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