Recommender systems have become essential tools for enhancing user experiences across various domains. While extensive research has been conducted on recommender systems for movies, music, and e-commerce, the rapidly growing and economically significant Non-Fungible Token (NFT) market remains underexplored. The unique characteristics and increasing prominence of the NFT market highlight the importance of developing tailored recommender systems to cater to its specific needs and unlock its full potential. In this paper, we examine the distinctive characteristics of NFTs and propose the first recommender system specifically designed to address NFT market challenges. In specific, we develop a Multi-Attention Recommender System for NFTs (NFT-MARS) with three key characteristics: (1) graph attention to handle sparse user-item interactions, (2) multi-modal attention to incorporate feature preference of users, and (3) multi-task learning to consider the dual nature of NFTs as both artwork and financial assets. We demonstrate the effectiveness of NFT-MARS compared to various baseline models using the actual transaction data of NFTs collected directly from blockchain for four of the most popular NFT collections. The source code and data are available at https://anonymous.4open.science/r/RecSys2023-93ED.

翻译：推荐系统已成为提升各领域用户体验的重要工具。尽管针对电影、音乐和电子商务的推荐系统已有大量研究，但增长迅速且具有重要经济价值的非同质化代币（NFT）市场仍未得到充分探索。NFT市场的独特特征及其日益突出的重要性，凸显了开发定制化推荐系统以满足其特定需求并释放其全部潜力的必要性。本文分析了NFT的独特特征，提出了首个专门针对NFT市场挑战的推荐系统。具体而言，我们开发了面向NFT的多注意力推荐系统（NFT-MARS），其具有三个关键特性：（1）图注意力机制以处理稀疏的用户-项目交互；（2）多模态注意力机制以融入用户特征偏好；（3）多任务学习以考虑NFT作为艺术品和金融资产的双重属性。我们使用直接从区块链收集的四个最热门NFT集合的实际交易数据，证明了NFT-MARS相较于多种基线模型的有效性。源代码和数据可在https://anonymous.4open.science/r/RecSys2023-93ED获取。