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Transparent, Scrutable and Explainable User Models for Personalized Recommendation

Authors:

Krisztian Balog,

Filip Radlinski,

Shushan ArakelyanAuthors Info & Claims

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 265 - 274

https://doi.org/10.1145/3331184.3331211

Published: 18 July 2019 Publication History

Abstract

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 methods have limited scrutability and transparency. For instance, if a user's interests change, then many item ratings would usually need to be modified to significantly shift the user's recommendations. Similarly, explaining how the system characterizes the user is impossible, short of presenting the entire list of known item ratings. In this paper, we present a new set-based recommendation technique that permits the user model to be explicitly presented to users in natural language, empowering users to understand recommendations made and improve the recommendations dynamically. While performing comparably to traditional collaborative filtering techniques in a standard static setting, our approach allows users to efficiently improve recommendations. Further, it makes it easier for the model to be validated and adjusted, building user trust and understanding.

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Index Terms

Transparent, Scrutable and Explainable User Models for Personalized Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2019

1512 pages

ISBN:9781450361729

DOI:10.1145/3331184

General Chairs:
Benjamin Piwowarski
CNRS - Sorbonne Universite, France
,
Max Chevalier
Universite de Toulouse, CNRS, France
,
Eric Gaussier
Universite Grenoble Alpes, CNRS, France
,
Program Chairs:
Yoelle Maarek
Amazon Research, Israel
,
Jian-Yun Nie
University of Montreal, Canada
,
Falk Scholer
RMIT University, Australia

Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 18 July 2019

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SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 21 - 25, 2019

Paris, France

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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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https://doi.org/10.3390/su16020781
Li YDeng XHu XLiu J(2024)The Effects of E-Commerce Recommendation System Transparency on Consumer Trust: Exploring Parallel Multiple Mediators and a ModeratorJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1904012619:4(2630-2649)Online publication date: 1-Oct-2024
https://doi.org/10.3390/jtaer19040126
Zhou YWang HHe JWang H(2024)Review-Based Explainable Recommendations: A Transparency PerspectiveACM Transactions on Recommender Systems10.1145/3701762Online publication date: 30-Oct-2024
https://doi.org/10.1145/3701762
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
Xu ZLamba HAi QTetreault JJaimes AOosterhuis HBast HXiong C(2024)CFE2: Counterfactual Editing for Search Result ExplanationProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672508(145-155)Online publication date: 2-Aug-2024
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Ain QChatti MMeteng Kamdem PAlatrash RJoarder SSiepmann C(2024)Learner Modeling and Recommendation of Learning Resources using Personal Knowledge GraphsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636881(273-283)Online publication date: 18-Mar-2024
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