short-paper

User Preferences for Hybrid Explanations

Authors:

James Schaffer,

John O'Donovan,

Lise GetoorAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 84 - 88

https://doi.org/10.1145/3109859.3109915

Published: 27 August 2017 Publication History

Abstract

Hybrid recommender systems combine several different sources of information to generate recommendations. These systems demonstrate improved accuracy compared to single-source recommendation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of explaining recommendations to users becomes more challenging. In this paper we describe a hybrid recommender system built on a probabilistic programming language, and discuss the benefits and challenges of explaining its recommendations to users. We perform a mixed model statistical analysis of user preferences for explanations in this system. Through an online user survey, we evaluate explanations for hybrid algorithms in a variety of text and visual, graph-based formats, that are either novel designs or derived from existing hybrid recommender systems.

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Cited By

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
Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657735
Aljuneidi SHeuten WAbdenebaoui LWolters MBoll S(2024)Why the Fine, AI? The Effect of Explanation Level on Citizens' Fairness Perception of AI-based Discretion in Public AdministrationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642535(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642535
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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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
Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657735
Aljuneidi SHeuten WAbdenebaoui LWolters MBoll S(2024)Why the Fine, AI? The Effect of Explanation Level on Citizens' Fairness Perception of AI-based Discretion in Public AdministrationsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642535(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642535
Larasati RDe Liddo AMotta E(2023)Meaningful Explanation Effect on User’s Trust in an AI Medical System: Designing Explanations for Non-Expert UsersACM Transactions on Interactive Intelligent Systems10.1145/363161413:4(1-39)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3631614
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: 31-Mar-2023
https://dl.acm.org/doi/10.1145/3589959
Lu HMa WWang YZhang MWang XLiu YChua TMa S(2023)User Perception of Recommendation Explanation: Are Your Explanations What Users Need?ACM Transactions on Information Systems10.1145/356548041:2(1-31)Online publication date: 25-Jan-2023
https://dl.acm.org/doi/10.1145/3565480
Kazi FSandbulte J(2023)Examining a social-based system with personalized recommendations to promote mental health for college studentsSmart Health10.1016/j.smhl.2023.10038528(100385)Online publication date: Jun-2023
https://doi.org/10.1016/j.smhl.2023.100385
Felfernig ATintarev NTrang Tran TStettinger M(2023)Explanations for GroupsGroup Recommender Systems10.1007/978-3-031-44943-7_6(109-131)Online publication date: 23-Sep-2023
https://doi.org/10.1007/978-3-031-44943-7_6
Hu ZBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Service-aware Recommendation and Justification of ResultsProceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3503252.3534357(341-345)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3503252.3534357
Chazette LKlos VHerzog FSchneider K(2022)Requirements on Explanations: A Quality Framework for Explainability2022 IEEE 30th International Requirements Engineering Conference (RE)10.1109/RE54965.2022.00019(140-152)Online publication date: Aug-2022
https://doi.org/10.1109/RE54965.2022.00019
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