research-article

On-demand Personalized Explanation for Transparent Recommendation

Authors:

Mohamed Amine Chatti,

Arham MuslimAuthors Info & Claims

UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

Pages 246 - 252

https://doi.org/10.1145/3450614.3464479

Published: 22 June 2021 Publication History

Abstract

The literature on explainable recommendations is already rich. In this paper, we aim to shed light on an aspect that remains under-explored in this area of research, namely providing personalized explanations. To address this gap, we developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations with varying levels of detail to meet the demands of different types of end-users. The results of a preliminary qualitative user study demonstrated potential benefits in terms of user satisfaction with the explainable recommender system. Our work would contribute to the literature on explainable recommendation by exploring the potential of on-demand personalized explanations, and contribute to the practice by offering suggestions for the design and appropriate use of personalized explanation interfaces in recommender systems.

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Cited By

Al-Ansari NAl-Thani DAl-Mansoori R(2024)User‐Centered Evaluation of Explainable Artificial Intelligence (XAI): A Systematic Literature ReviewHuman Behavior and Emerging Technologies10.1155/2024/46288552024:1Online publication date: 15-Jul-2024
https://doi.org/10.1155/2024/4628855
Jafari EBoratto LGena CMarras MGermanakos PPopescus E(2024)Balanced Explanations in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664915(25-29)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664915
Jafari EVassileva J(2024)Designing Effective Warnings for Manipulative Designs in Mobile ApplicationsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659550(12-17)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659550
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On-demand Personalized Explanation for Transparent Recommendation

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Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

June 2021

431 pages

ISBN:9781450383677

DOI:10.1145/3450614

Editors:
Judith Masthoff
Utrecht University, The Netherlands and University of Aberdeen, UK
,
Eelco Herder
Radboud University, The Netherlands
,
Nava Tintarev
University of Maastricht, The Netherlands
,
Marko Tkalčič
University of Primorska, Slovenia

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 June 2021

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UMAP '21

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UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization

June 21 - 25, 2021

Utrecht, Netherlands

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Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Al-Ansari NAl-Thani DAl-Mansoori R(2024)User‐Centered Evaluation of Explainable Artificial Intelligence (XAI): A Systematic Literature ReviewHuman Behavior and Emerging Technologies10.1155/2024/46288552024:1Online publication date: 15-Jul-2024
https://doi.org/10.1155/2024/4628855
Jafari EBoratto LGena CMarras MGermanakos PPopescus E(2024)Balanced Explanations in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664915(25-29)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664915
Jafari EVassileva J(2024)Designing Effective Warnings for Manipulative Designs in Mobile ApplicationsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659550(12-17)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659550
Guesmi MChatti MJoarder SAin QSiepmann CGhanbarzadeh HAlatrash R(2023)Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender SystemInformation10.3390/info1407040114:7(401)Online publication date: 14-Jul-2023
https://doi.org/10.3390/info14070401
You YTsai CLi YMa FHeron CGui X(2023)Beyond Self-diagnosis: How a Chatbot-based Symptom Checker Should RespondACM Transactions on Computer-Human Interaction10.1145/358995930:4(1-44)Online publication date: 11-Sep-2023
https://dl.acm.org/doi/10.1145/3589959
Guesmi MChatti MJoarder SAin QAlatrash RSiepmann CVahidi T(2023)Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226279740:22(7248-7269)Online publication date: 15-Oct-2023
https://doi.org/10.1080/10447318.2023.2262797
Guesmi MChatti MTayyar AAin QJoarder S(2022)Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design ApproachMultimodal Technologies and Interaction10.3390/mti60600426:6(42)Online publication date: 30-May-2022
https://doi.org/10.3390/mti6060042
Sun YSundar S(2022)Exploring the Effects of Interactive Dialogue in Improving User Control for Explainable Online Symptom CheckersExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519668(1-7)Online publication date: 27-Apr-2022
https://dl.acm.org/doi/10.1145/3491101.3519668
Shulner-Tal AKuflik TKliger D(2022)Enhancing Fairness Perception – Towards Human-Centred AI and Personalized Explanations Understanding the Factors Influencing Laypeople’s Fairness Perceptions of Algorithmic DecisionsInternational Journal of Human–Computer Interaction10.1080/10447318.2022.209570539:7(1455-1482)Online publication date: 19-Jul-2022
https://doi.org/10.1080/10447318.2022.2095705

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