article

Generating Personalized Explanations for Recommender Systems Using a Knowledge Base

Authors:

Alfrin Letus SaldanhaAuthors Info & Claims

International Journal of Multimedia Data Engineering and Management (IJMDEM), Volume 12, Issue 4

Pages 20 - 37

https://doi.org/10.4018/IJMDEM.2021100102

Published: 01 October 2021 Publication History

Abstract

In the last decade, we have seen an increase in the need for interpretable recommendations. Explaining why a product is recommended to a user increases user trust and makes the recommendations more acceptable. The authors propose a personalized explanation generation system, PEREXGEN (personalized explanation generation) that generates personalized explanations for recommender systems using a model-agnostic approach. The proposed model consists of a recommender and an explanation module. Since they implement a model-agnostic approach to generate personalized explanations, they focus more on the explanation module. The explanation module consists of a task-specialized item knowledge graph (TSI-KG) generation from a knowledge base and an explanation generation component. They employ the MovieLens and Wikidata datasets and evaluate the proposed system's model-agnostic properties using conventional and state-of-the-art recommender systems. The user study shows that PEREXGEN generates more persuasive and natural explanations.

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Cited By

Luo SHan HChang QMiyazaki JHong JLanperne MPark JCerny TShahriar H(2023)Automatic Extraction of Effective Relations in Knowledge Graph for a Recommendation Explanation SystemProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577732(1754-1761)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577732
Brusilovsky Pde Gemmis MFelfernig ALops PPolignano MSemeraro GWillemsen M(2022)Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’22)Proceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547413(667-670)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547413

Index Terms

Generating Personalized Explanations for Recommender Systems Using a Knowledge Base
1. Computing methodologies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications

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

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cover image International Journal of Multimedia Data Engineering & Management

International Journal of Multimedia Data Engineering & Management Volume 12, Issue 4

Oct 2021

71 pages

ISSN:1947-8534

EISSN:1947-8542

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Copyright © 2021.

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IGI Global

United States

Publication History

Published: 01 October 2021

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Cited By

Luo SHan HChang QMiyazaki JHong JLanperne MPark JCerny TShahriar H(2023)Automatic Extraction of Effective Relations in Knowledge Graph for a Recommendation Explanation SystemProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577732(1754-1761)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577732
Brusilovsky Pde Gemmis MFelfernig ALops PPolignano MSemeraro GWillemsen M(2022)Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’22)Proceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547413(667-670)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547413

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