A Framework for Recommendation Algorithms Using Knowledge Graph and Random Walk Methods

A number of knowledge graph (KG)-based recommendation algorithms have been introduced; KGs enable users and items and their attributes to be treated in an integrated way and structural information to be captured through graphs. There are many variations of the KG based recommendation algorithms. Among them, KG embedding is often used, but doing this does not take advantage of the meta-path-level proximity between users and items. This paper presents a flexible framework combining random walk and KG embedding methods. The random walk model is formulated on the basis of the similarity between nodes revealed by the KG embedding. This enables the metapath level proximity of users and items to be efficiently utilized. Comparison testing demonstrated that the proposed framework performs better than random- walk-only methods and KG-embedding-only methods, and slightly better than the existing method we have extended.