A Multi-Modal Dataset for NFT Recommendation Systems

Abstract

There is a lack of standardized datasets for NFT (Non-Fungible Token) recommendation systems. This study presents a comprehensive dataset designed for NFT recommendation systems, incorporating both NFT-related data (e.g., images, textual descriptions, rarity and transaction data) and user-related data (e.g., purchase price, transaction duration, and NFT holding period). To create the dataset, a Data Collection Tool was developed to gather raw data via the OpenSea API, and a Data Preparation Tool was implemented for preprocessing and filtering. All data used in this study are publicly available and anonymized, ensuring that user privacy is fully preserved. The dataset is evaluated using NFT-NCFAE, a deep learningbased NFT recommendation model, with performance measured by Recall and NDCG evaluation metrics. The evaluation results demonstrate the suitability and value of the proposed dataset for NFT recommendation systems. By making the dataset and its associated tools publicly available, this work aims to establish a benchmark for future research and enable comparability across different models.