Explainable Recommendation Using Knowledge Graphs and Random Walks

Kaname Muto, Satoshi Oyama, Itsuki Noda

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.