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Personalized explanations for hybrid recommender systems

Authors:

James Schaffer,

John O'Donovan,

Lise GetoorAuthors Info & Claims

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages 379 - 390

https://doi.org/10.1145/3301275.3302306

Published: 17 March 2019 Publication History

Abstract

Recommender systems have become pervasive on the web, shaping the way users see information and thus the decisions they make. As these systems get more complex, there is a growing need for transparency. In this paper, we study the problem of generating and visualizing personalized explanations for hybrid recommender systems, which incorporate many different data sources. We build upon a hybrid probabilistic graphical model and develop an approach to generate real-time recommendations along with personalized explanations. To study the benefits of explanations for hybrid recommender systems, we conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. We experiment with 1) different explanation styles (e.g., user-based, item-based), 2) manipulating the number of explanation styles presented, and 3) manipulating the presentation format (e.g., textual vs. visual). We apply a mixed model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format.

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Cited By

Okoso AOtaki KKoide SBaba Y(2025)Impact of Tone-Aware Explanations in Recommender SystemsACM Transactions on Recommender Systems10.1145/3718101Online publication date: 15-Feb-2025
https://doi.org/10.1145/3718101
Naveed SStevens GRobin-Kern D(2024)An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAIApplied Sciences10.3390/app14231128814:23(11288)Online publication date: 3-Dec-2024
https://doi.org/10.3390/app142311288
Schütze CRichter BLammert OThommes KWrede B(2024)Static Socio-demographic and Individual Factors for Generating Explanations in XAI: Can they serve as a prior in DSS for adaptation of explanation strategies?Proceedings of the 12th International Conference on Human-Agent Interaction10.1145/3687272.3688300(141-149)Online publication date: 24-Nov-2024
https://dl.acm.org/doi/10.1145/3687272.3688300
Show More Cited By

Index Terms

Personalized explanations for hybrid recommender systems
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social networking sites
  2. Visualization
    1. Empirical studies in visualization
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering
    2. Decision support systems

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cover image ACM Conferences

IUI '19: Proceedings of the 24th International Conference on Intelligent User Interfaces

March 2019

713 pages

ISBN:9781450362726

DOI:10.1145/3301275

General Chairs:
Wai-Tat Fu,
Shimei Pan,
Program Chairs:
Oliver Brdiczka,
Polo Chau,
Gaelle Calvary

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States 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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Published: 17 March 2019

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IUI '19: 24th International Conference on Intelligent User Interfaces

March 17 - 20, 2019

California, Marina del Ray

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IUI '19 Paper Acceptance Rate 71 of 282 submissions, 25%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Okoso AOtaki KKoide SBaba Y(2025)Impact of Tone-Aware Explanations in Recommender SystemsACM Transactions on Recommender Systems10.1145/3718101Online publication date: 15-Feb-2025
https://doi.org/10.1145/3718101
Naveed SStevens GRobin-Kern D(2024)An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAIApplied Sciences10.3390/app14231128814:23(11288)Online publication date: 3-Dec-2024
https://doi.org/10.3390/app142311288
Schütze CRichter BLammert OThommes KWrede B(2024)Static Socio-demographic and Individual Factors for Generating Explanations in XAI: Can they serve as a prior in DSS for adaptation of explanation strategies?Proceedings of the 12th International Conference on Human-Agent Interaction10.1145/3687272.3688300(141-149)Online publication date: 24-Nov-2024
https://dl.acm.org/doi/10.1145/3687272.3688300
Wang YChu CLi K(2024)Research on Game Recommendation Algorithms Based on Hybrid ModelsProceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering10.1145/3672758.3672803(267-274)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3672758.3672803
Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Alsayed ADam HNguyen CSpinellis DConstantinou EBacchelli A(2024)MicroRec: Leveraging Large Language Models for Microservice RecommendationProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644916(419-430)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644916
Tran TPolat Erdeniz SFelfernig ALubos SEl Mansi MLe V(2024)Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691708(1108-1112)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691708
Shashaani S(2024)Explainability in Music Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688028(1395-1401)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688028
Jeng J(2024)Bridging Viewpoints in News with Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688008(1283-1289)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688008
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
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