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.

翻译：非同质化代币（NFT）是一种存储在区块链上的可交易数据单元，可作为数字资产的所有权凭证。过去几年见证了NFT市场的指数级增长。2021年，NFT市场达到顶峰，交易额超过400亿美元。尽管NFT市场蓬勃发展，但大多数相关研究聚焦于其技术层面，如标准、协议和安全性，而本研究致力于为NFT买家开发一款开创性的推荐系统。本文介绍了一种基于极深分解机（xDeepFM）的推荐系统NFT.mine，该系统实现了实时数据采集、数据清洗、特征提取、训练与推理。我们使用最具影响力的NFT交易平台OpenSea的数据来测试NFT.mine的性能。实验结果表明，与逻辑回归、朴素贝叶斯、随机森林等传统模型相比，NFT.mine以更高的AUC和更低的交叉熵损失表现更优，并为NFT买家输出个性化推荐。