Using Semantic Relations for Content-based Recommender Systems in Cultural Heritage

Metadata vocabularies provide various semantic relations between concepts. For content-based recommender systems, these relations enable a wide range of concepts to be recommended. However, not all semantically related concepts are interesting for end users. In this paper, we identified a number of semantic relations, which are within one vocabulary (e.g. a concept has a broader/narrower concept) and across multiple vocabularies (e.g. an artist is associated to an art style). Our goal is to investigate which semantic relations are useful for recommendations of art concepts and to look at the combined use of artwork features and semantic relations in sequence. These sequences of ratings allow us to derive some navigation patterns from users, which might enhance the accuracy of recommendations and be reused for other recommender systems in similar domains. We tested the CHIP demonstrator, called the Art Recommender with end users by recommending both semanticallyrelated concepts and artworks features (e.g.creator, material, subject).