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.

References

[1]

Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, 2019. One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques. arXiv preprint arXiv:1909.03012(2019).

[2]

Shuo Chang, F Maxwell Harper, and Loren Gilbert Terveen. 2016. Crowd-based personalized natural language explanations for recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. 175–182.

Digital Library

[3]

Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2018. Explaining Recommendations by Means of User Reviews. In IUI Workshops.

[4]

Haiyan Fan and Marshall Scott Poole. 2006. What is personalization? Perspectives on the design and implementation of personalization in information systems. Journal of Organizational Computing and Electronic Commerce 16, 3-4(2006), 179–202.

[5]

Fatih Gedikli, Dietmar Jannach, and Mouzhi Ge. 2014. How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies 72, 4 (2014), 367–382.

Digital Library

[6]

Mouadh Guesmi, mohamed Amine Chatti, and Arham Muslim. 2020. A Review of Explanatory Visualizations in Recommender Systems. In Companion Proceedings 10th International Conference on Learning Analytics and Knowledge (LAK20). 480–491.

[7]

Alexander Jung and Pedro HJ Nardelli. 2020. An information-theoretic approach to personalized explainable machine learning. IEEE Signal Processing Letters 27 (2020), 825–829.

[8]

Pigi Kouki, James Schaffer, Jay Pujara, John O’Donovan, and Lise Getoor. 2019. Personalized explanations for hybrid recommender systems. In Proceedings of the 24th International Conference on Intelligent User Interfaces. 379–390.

Digital Library

[9]

Pigi Kouki, James Schaffer, Jay Pujara, John O’Donovan, and Lise Getoor. 2020. Generating and Understanding Personalized Explanations in Hybrid Recommender Systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 10, 4(2020), 1–40.

Digital Library

[10]

NIklas Kuhl, Jodie Lobana, and Christian Meske. 2020. Do you comply with AI?–Personalized explanations of learning algorithms and their impact on employees’ compliance behavior. arXiv preprint arXiv:2002.08777(2020).

[11]

Yichao Lu, Ruihai Dong, and Barry Smyth. 2018. Why I like it: multi-task learning for recommendation and explanation. In Proceedings of the 12th ACM Conference on Recommender Systems. 4–12.

Digital Library

[12]

James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, and Rishabh Mehrotra. 2018. Explore, exploit, and explain: personalizing explainable recommendations with bandits. In Proceedings of the 12th ACM conference on recommender systems. 31–39.

Digital Library

[13]

Martijn Millecamp, Nyi Nyi Htun, Cristina Conati, and Katrien Verbert. 2019. To explain or not to explain: the effects of personal characteristics when explaining music recommendations. In Proceedings of the 24th International Conference on Intelligent User Interfaces. 397–407.

Digital Library

[14]

Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267 (2019), 1–38.

[15]

Sina Mohseni, Niloofar Zarei, and Eric D Ragan. 2018. A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems. arXiv (2018), arXiv–1811.

[16]

Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2016. ExpLOD: a framework for explaining recommendations based on the linked open data cloud. In Proceedings of the 10th ACM Conference on Recommender Systems. 151–154.

Digital Library

[17]

Ingrid Nunes and Dietmar Jannach. 2017. A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction 27, 3-5 (2017), 393–444.

Digital Library

[18]

Johanes Schneider and Joshua Handali. 2019. Personalized explanation in machine learning: A conceptualization. arXiv preprint arXiv:1901.00770(2019).

[19]

Martin Svrcek, Michal Kompan, and Maria Bielikova. 2019. Towards understandable personalized recommendations: Hybrid explanations. Computer Science and Information Systems 16, 1 (2019), 179–203.

[20]

Nava Tintarev and Judith Masthoff. 2012. Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction 22, 4-5 (2012), 399–439.

Digital Library

[21]

Nava Tintarev and Judith Masthoff. 2015. Explaining recommendations: Design and evaluation. In Recommender systems handbook. Springer, 353–382.

[22]

Yongfeng Zhang and Xu Chen. 2018. Explainable recommendation: A survey and new perspectives. arXiv preprint arXiv:1804.11192(2018).

[23]

Ruijing Zhao, Izak Benbasat, and Hasan Cavusoglu. 2019. Do users always want to know more? Investigating the relationship between system transparency and users’ trust in advice-giving systems. In Proceedings of the 27th European Conference on Information Systems.

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
Show More Cited By

Index Terms

On-demand Personalized Explanation for Transparent Recommendation

Index terms have been assigned to the content through auto-classification.

Recommendations

Transparent, Scrutable and Explainable User Models for Personalized Recommendation
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Most recommender systems base their recommendations on implicit or explicit item-level feedback provided by users. These item ratings are combined into a complex user model, which then predicts the suitability of other items. While effective, such ...
Crowd-Based Personalized Natural Language Explanations for Recommendations
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people ...
Personalized news recommendation based on click behavior
IUI '10: Proceedings of the 15th international conference on Intelligent user interfaces

Online news reading has become very popular as the web provides access to news articles from millions of sources around the world. A key challenge of news websites is to help users find the articles that are interesting to read. In this paper, we ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

UMAP '21

Sponsor:

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

June 21 - 25, 2021

Utrecht, Netherlands

Acceptance Rates

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
469
Total Downloads

Downloads (Last 12 months)124
Downloads (Last 6 weeks)15

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents