NFTs to MARS: Multi-Attention Recommender System for NFTs
This work addresses the underexplored NFT market by developing a tailored recommender system, which is incremental as it adapts existing techniques like attention and multi-task learning to a new domain.
The paper tackles the problem of recommending Non-Fungible Tokens (NFTs) by proposing NFT-MARS, a multi-attention recommender system that addresses sparse interactions and multi-modal features, demonstrating effectiveness on actual blockchain transaction data from four popular NFT collections.
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.