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

Elisabeth Lex, Markus Schedl

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

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.

Originalspracheenglisch
TitelCHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval
Herausgeber (Verlag)Association of Computing Machinery
Seiten367-368
Seitenumfang2
ISBN (elektronisch)9781450391863
DOIs
PublikationsstatusVeröffentlicht - 14 März 2022
Veranstaltung7th ACM SIGIR Conference on Human Information Interaction and Retrieval: CHIIR 2022 - Virtuell, Deutschland
Dauer: 14 März 202218 März 2022

Konferenz

Konferenz7th ACM SIGIR Conference on Human Information Interaction and Retrieval
KurztitelCHIIR 2022
Land/GebietDeutschland
OrtVirtuell
Zeitraum14/03/2218/03/22

ASJC Scopus subject areas

  • Human-computer interaction
  • Information systems

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