An Analysis of Knowledge Representation for Anime Recommendation Using Graph Neural Networks

Yuki Saito, Shusaku Egami, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga

Abstract: In recent years, entertainment content, such as movies, music, and anime, has been gaining attention due to the stay-at-home demand caused by the expansion of COVID-19. In the content domain, research in the field of knowledge representation is primarily concerned with accurately describing metadata. Therefore, different knowledge representations are required for applications in downstream tasks. In this study, we aim to clarify effective knowledge representation through a case study of recommending anime works. Thus, we hypothesized how to represent anime works knowledge to improve recommendation performance from both quantitative and qualitative aspects and verified the hypotheses by changing the knowledge representation structure according to the hypothesis. Initially, we collected data about anime works from multiple data sources and integrated them to construct a knowledge graph (KG). We also prepared several KGs by varying the knowledge configuration. Subsequently, we compared