Music curator recommendations using linked data

People who collect content by human power and create criticism are called curators. Recently, the number of music curators has been increasing. However, it is often difficult to discover a music curator suited to the user's personal taste. Fortunately, linked data, which involve a large network structure to link data, exist. Using a Linked Data Semantic Distance algorithm that utilized linked data, Passant calculated the distance between different pieces of music. In this paper, we propose a method for recommending a music curator who suits the user's taste using linked data. A link structure is formed using the listening history of the user, the music curator's musical criticism data, and music information data. We calculate the distance between the user and the music curator using the linked data.