  Recommender systems have been actively studied and applied in various domains
to deal with information overload. Although there are numerous studies on
recommender systems for movies, music, and e-commerce, comparatively less
attention has been paid to the recommender system for NFTs despite the
continuous growth of the NFT market. This paper presents a recommender system
for NFTs that utilizes a variety of data sources, from NFT transaction records
to external item features, to generate precise recommendations that cater to
individual preferences. We develop a data-efficient graph-based recommender
system to efficiently capture the complex relationship between each item and
users and generate node(item) embeddings which incorporate both node feature
information and graph structure. Furthermore, we exploit inputs beyond
user-item interactions, such as image feature, text feature, and price feature.
Numerical experiments verify the performance of the graph-based recommender
system improves significantly after utilizing all types of item features as
side information, thereby outperforming all other baselines.
