Evaluation of the User-Centric Explanation Strategies for Interactive Recommenders

Berk Buzcu*, Emre Kuru, Davide Calvaresi, Reyhan Aydoğan

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

As recommendation systems become increasingly prevalent in numerous fields, the need for clear and persuasive interactions with users is rising. Integrating explainability into these systems is emerging as an effective approach to enhance user trust and sociability. This research focuses on recommendation systems that utilize a range of explainability techniques to foster trust by providing understandable personalized explanations for the recommendations made. In line with this, we study three distinct explanation methods that correspond with three basic recommendation strategies and assess their efficacy through user experiments. The findings from the experiments indicate that the majority of participants value the suggested explanation styles and favor straightforward, concise explanations over comparative ones.

Original languageEnglish
Title of host publicationExplainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Revised Selected Papers
Subtitle of host publicationConference Proceedings
EditorsDavide Calvaresi, Amro Najjar, Andrea Omicini, Rachele Carli, Giovanni Ciatto, Reyhan Aydogan, Joris Hulstijn, Kary Främling
Place of PublicationCham
PublisherSpringer
Pages21-38
Number of pages18
ISBN (Electronic)978-3-031-70074-3
ISBN (Print)978-3-031-70073-6
DOIs
Publication statusPublished - 2024
Event6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems, EXTRAAMAS 2024 - Auckland, New Zealand
Duration: 6 May 202410 May 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14847 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems, EXTRAAMAS 2024
Country/TerritoryNew Zealand
CityAuckland
Period6/05/2410/05/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Explainable Recommendations
  • Explanation Strategies
  • User Studies

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