Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems

Elisabeth Lex; Markus Schedl

doi:10.1145/3498366.3505841

Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems

Elisabeth Lex, Markus Schedl

Institute of Interactive Systems and Data Science (7060)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Personalized recommender systems are essential tools to facilitate human decision making. Many contemporary recommender systems use advanced machine learning techniques to model and predict user preferences from behavioral data. While such systems can provide helpful recommendations, their algorithms' design does not incorporate the underlying psychological mechanisms that shape user preferences and behavior. In this tutorial, we will guide the attendees through the state-of-The-Art in psychology-informed recommender systems, i.e., recommender systems that consider extrinsic and intrinsic human factors. We show how such systems can improve the recommendation process in a user-centric fashion.

Original language	English
Title of host publication	CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval
Publisher	Association of Computing Machinery
Pages	367-368
Number of pages	2
ISBN (Electronic)	9781450391863
DOIs	https://doi.org/10.1145/3498366.3505841
Publication status	Published - 14 Mar 2022
Event	7th ACM SIGIR Conference on Human Information Interaction and Retrieval: CHIIR 2022 - Virtuell, Germany Duration: 14 Mar 2022 → 18 Mar 2022

Conference

Conference	7th ACM SIGIR Conference on Human Information Interaction and Retrieval
Abbreviated title	CHIIR 2022
Country/Territory	Germany
City	Virtuell
Period	14/03/22 → 18/03/22

Keywords

affect
cognitive models
emotion
human decision making
personality
recommender systems
user-centric evaluation

ASJC Scopus subject areas

Human-Computer Interaction
Information Systems

Access to Document

10.1145/3498366.3505841

Cite this

Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems. / Lex, Elisabeth; Schedl, Markus.
CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval. Association of Computing Machinery, 2022. p. 367-368.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Lex, E & Schedl, M 2022, Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems. in CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval. Association of Computing Machinery, pp. 367-368, 7th ACM SIGIR Conference on Human Information Interaction and Retrieval, Virtuell, Germany, 14/03/22. https://doi.org/10.1145/3498366.3505841

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abstract = "Personalized recommender systems are essential tools to facilitate human decision making. Many contemporary recommender systems use advanced machine learning techniques to model and predict user preferences from behavioral data. While such systems can provide helpful recommendations, their algorithms' design does not incorporate the underlying psychological mechanisms that shape user preferences and behavior. In this tutorial, we will guide the attendees through the state-of-The-Art in psychology-informed recommender systems, i.e., recommender systems that consider extrinsic and intrinsic human factors. We show how such systems can improve the recommendation process in a user-centric fashion. ",

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Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this