Non-fungible tokens (NFTs) are digital assets stored on a blockchain
representing real-world objects such as art or collectibles. It is a
multibillion-dollar market, where the number of NFT collections increased over
100% in 2022; there are currently more than 80K collections on the Ethereum
blockchain. Each collection, containing numerous tokens of a particular theme,
has its unique characteristics. In this paper, we take a contextual generative
approach that learns these diverse characteristics of NFT collections and
generates the potential market value predictions of newly minted ones. We model
NFTs as a series of transactions. First, meaningful contexts capturing the
characteristics of various collections are derived using unsupervised learning.
Next, our generative approach leverages these contexts to learn better
characterizations of established NFT collections with differing market
capitalization values. Finally, given a new collection in an early stage, the
approach generates future transaction series for this emerging collection.
Comprehensive experiments demonstrate that our approach closely predicts the
potential value of NFT collections