Abstract
Recommender systems have been gaining attention in recent decades for the ability to ease information overload. One of the main areas of concern is the explainability of recommender systems. In this paper, we propose a model-agnostic recommendation explanation system, which can improve the explainability of existing recommender systems. In the proposed system, a task-specialized knowledge graph is introduced, and the explanation is generated based on the paths between the recommended item and the user’s history of interacted items. Finally, we implemented the proposed system using Wikidata and the MovieLens dataset. Through several case studies, we show that our system can provide more convincing and diverse personalized explanations for recommended items compared with existing systems.
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Acknowledgments
This work was partly supported by JSPS KAKENHI Grant Numbers 18H03242, 18H03342, and 19H01138.
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Chen, Y., Miyazaki, J. (2020). A Model-Agnostic Recommendation Explanation System Based on Knowledge Graph. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12392. Springer, Cham. https://doi.org/10.1007/978-3-030-59051-2_10
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