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arXiv · 2306.03942

NFT.mine: An xDeepFM-based Recommender System for Non-fungible Token (NFT) Buyers

Shuwei Li·Yucheng Jin·Pin-Lun Hsu·Ya-Sin Luo
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Abstract

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.

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Shuwei Li, Yucheng Jin, Pin-Lun Hsu, Ya-Sin Luo. 2023-06-06. NFT.mine: An xDeepFM-based Recommender System for Non-fungible Token (NFT) Buyers. https://arxiv.org/abs/2306.03942

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