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Explainable Recommendation Using Knowledge Graphs and Random Walks

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/88665

Title: Explainable Recommendation Using Knowledge Graphs and Random Walks
Authors: Muto, Kaname Browse this author
Oyama, Satoshi Browse this author →KAKEN DB
Noda, Itsuki Browse this author →KAKEN DB
Keywords: Recommendation
Knowledge Graph Embedding
Random Walk
Explainability
Issue Date: 17-Dec-2022
Publisher: IEEE
Journal Title: 2022 IEEE International Conference on Big Data (Big Data)
Volume: 2022
Start Page: 4028
End Page: 4032
Publisher DOI: 10.1109/BigData55660.2022.10021120
Abstract: A knowledge graph (KG) contains rich information about users and items. The relationship among users and items can help to generate intuitive explanations for recommended items. Many variations of KG-based recommendation algorithms use the shortest path from the user to the item in order to generate an explanation of the recommendation. However, the simple shortest path may not be useful in the case when the path is long, because the interpretation of the long path is difficult. Also, there may be no path between the user and the recommended item. In order to overcome these difficulties, we proposed an extension of the existing framework based on random walk with KG embedding. In the proposed framework, we use the most probable path in a random walk as an explanation. Thereby, our framework can even explain items that have no connection in the KG due to the latent connection resulting from random walk teleportation. Comparison experiment demonstrated that the framework can provide more suitable recommendations than the existing method. In addition, the experiment show the ability of the proposed method to generate explanation for all recommendations that have no path in the graph.
Description: 2022 IEEE International Conference on Big Data (Big Data). 17-20 December 2022, Osaka International Convention Center (OICC),Osaka.
Conference Name: IEEE International Conference on Big Data (Big Data)
Conference Sequence: 2022
Conference Place: Osaka
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: proceedings (author version)
URI: http://hdl.handle.net/2115/88665
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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