Predicting NFT Classification with GNN: A Recommender System for Web3 Assets

The development of effective recommender systems for Web3 assets, such as the Non-Fungible Token (NFT), requires concentration along with the growth of popularity and heterogeneity in many potential applications such as Web3 gaming and NFT rental markets, the requirements of predicting rNFT classification desire a practical solution. In this paper, we make use of the referable NFT (rNFT
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In this work, rNFT mainly refers to the EIP-5521 protocol and corresponding formed network/topology [1], while NFT is used in the context of a single node, node sets, or products that align with the EIP-5521 protocol.) standard [2], indexed EIP-5521, to construct an rNFT classification framework leveraging Graph Neural Network (GNN), an emerging branch of Deep Learning (DL), which learns on the inherent topology of graph-based data. In particular, we first transform the rNFT backward and onward reference relationship to a Direct Acyclic Graph (DAG) and model appropriate node and edge features from rNFT metadata and associated token transactions. Next, a multi-layer GraphSage model is designed to include the collected features for the learning process. In this way, the model takes into account graph topology together with features to classify both the existing and incoming NFT nodes in a supervised way. We also give comprehensive elaboration on the architecture of the new GNN-based recommender system with discussions in regard to its characteristics and challenges. Furthermore, we expect to conduct extensive experiments, by presenting an initial plan, to show the feasibility and efficacy of our system.