Generating Personalized Explanations for Recommender Systems Using a Knowledge Base

Yuhao Chen, Shi-Jun Luo, Hyoil Han, Jun Miyazaki and Alfrin Letus Saldanha
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Yuhao Chen: Tokyo Institute of Technology, Japan
Shi-Jun Luo: Tokyo Institute of Technology, Japan
Hyoil Han: Illinois State University, USA
Jun Miyazaki: Tokyo Institute of Technology, Japan
Alfrin Letus Saldanha: Illinois State University, USA

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2021, vol. 12, issue 4, 20-37

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.

Date: 2021
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