  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.
