A Recommender System for NFT Collectibles with Item Feature
This addresses the need for personalized recommendations in the growing NFT market, but it is incremental as it applies existing graph-based methods to a new domain with added features.
The paper tackles the problem of recommending NFT collectibles by developing a graph-based recommender system that uses transaction records and external item features like images, text, and price. The result shows significant performance improvements, outperforming all baselines in numerical experiments.
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.