Transfer öğrenme görsel özellikleri ile NFT satış özellikleri ve fiyat tahmini

Non-fungible tokens~(NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. NFTs are almost always traded by using cryptocurrencies such as Ethereum and blockchains are utilized to encode them. There was a recent surge of interest in trading them which makes them another type of alternative investment. While the existing research predominantly focuses on the technical aspects of NFTs, limited attention has been given to predicting their prices. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, subjectivity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework. Here, we focus on two related prediction problems over NFTs: Predicting NFTs' sale price, and inferring whether a given NFT will participate in a secondary sale. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, trader behavior, seller's and buyer's centralities in the trading network, and collection's resale probability, and we assess the reliability of these features. We categorize input NFTs into six categories based on their characteristic features: Art, Collectibles, Games, Metaverse, Utility, and Others. We train several different machine learning methods on these attributes to answer these two questions. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, even though the pre-trained model giving the optimal result may change depending on the problem type. In general, none of the learning algorithms consistently outperformed the rest of them across all categories. Our code is publicly available at https://github.com/seferlab/deep_nft.