  Non-fungible token (NFT) is a tradable unit of data stored on the blockchain
which can be associated with some digital asset as a certification of
ownership. The past several years have witnessed the exponential growth of the
NFT market. In 2021, the NFT market reached its peak with more than $40 billion
trades. Despite the booming NFT market, most NFT-related studies focus on its
technical aspect, such as standards, protocols, and security, while our study
aims at developing a pioneering recommender system for NFT buyers. In this
paper, we introduce an extreme deep factorization machine (xDeepFM)-based
recommender system, NFT.mine, which achieves real-time data collection, data
cleaning, feature extraction, training, and inference. We used data from
OpenSea, the most influential NFT trading platform, to testify the performance
of NFT.mine. As a result, experiments showed that compared to traditional
models such as logistic regression, naive Bayes, random forest, etc., NFT.mine
outperforms them with higher AUC and lower cross entropy loss and outputs
personalized recommendations for NFT buyers.
