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Generating Personalized Explanations for Recommender Systems Using a Knowledge Base

Generating Personalized Explanations for Recommender Systems Using a Knowledge Base

Yuhao Chen, Shi-Jun Luo, Hyoil Han, Jun Miyazaki, Alfrin Letus Saldanha
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 18
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781799860518|DOI: 10.4018/IJMDEM.2021100102
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MLA

Chen, Yuhao, et al. "Generating Personalized Explanations for Recommender Systems Using a Knowledge Base." IJMDEM vol.12, no.4 2021: pp.20-37. http://doi.org/10.4018/IJMDEM.2021100102

APA

Chen, Y., Luo, S., Han, H., Miyazaki, J., & Saldanha, A. L. (2021). Generating Personalized Explanations for Recommender Systems Using a Knowledge Base. International Journal of Multimedia Data Engineering and Management (IJMDEM), 12(4), 20-37. http://doi.org/10.4018/IJMDEM.2021100102

Chicago

Chen, Yuhao, et al. "Generating Personalized Explanations for Recommender Systems Using a Knowledge Base," International Journal of Multimedia Data Engineering and Management (IJMDEM) 12, no.4: 20-37. http://doi.org/10.4018/IJMDEM.2021100102

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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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