Authors:
Yuki Saito
1
;
Shusaku Egami
1
;
2
;
Yuichi Sei
1
;
Yasuyuki Tahara
1
and
Akihiko Ohsuga
1
Affiliations:
1
Department of Informatics, University of Electro-Communications 1–5–1 Chofugaoka, Chofu, Tokyo, 182–8585, Japan
;
2
National Institute of Advanced Industrial Science and Technology (AIST), 2–4–7 Aomi, Koto-ku, Tokyo, 135–0064, Japan
Keyword(s):
Knowledge Graph, Graph Neural Networks, Recommender System.
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
the recommendation performance of each KG as an input to the graph neural networks. As a result, it was found that the amount of semantic relationships was proportional to the recommendation performance and that the properties that can characterize the work contributed to the recommendation.
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