mlsanityData to 7 Oct 2026

NFTs to MARS: Multi-Attention Recommender System for NFTs

arXiv 2306.10053 ↗First posted 13 Jun 2023cs.IR cs.AI econ.GNcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 13 Jun 2023.

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14 of 34 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  4. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  5. 2017Neural Collaborative Filteringcited by 1,108
  6. 2016Wide & Deep Learning for Recommender Systemscited by 745
  7. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  8. 2017Deep & Cross Network for Ad Click Predictionscited by 392
  9. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 319
  10. 2022ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterestcited by 23
  11. 2021Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendationcited by 18
  12. 2021Personalized Embedding-based e-Commerce Recommendations at eBaycited by 12
2 more
  1. 2022An Analysis of the Features Considerable for NFT Recommendationscited by 2
  2. 2022Exploration of the possibility of infusing Social Media Trends into generating NFT Recommendationscited by 2

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