# Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction

arXiv: https://arxiv.org/abs/2302.01676 · HTML: https://mlsanity.com/paper/2302.01676/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2105.07447/) (2021)
- [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021)
- [TweetBoost: Influence of Social Media on NFT Valuation](https://mlsanity.com/paper/2201.08373/) (2022)
- [NFT Wash Trading: Quantifying suspicious behaviour in NFT markets](https://mlsanity.com/paper/2202.03866/) (2022)
- [Heterogeneous rarity patterns drive price dynamics in NFT collections](https://mlsanity.com/paper/2204.10243/) (2022)
- [Fairness in ERC token markets: A Case Study of CryptoKitties](https://mlsanity.com/paper/2102.03721/) (2021)

## Built on by (most cited first, 6 of 6)

- [Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning](https://mlsanity.com/paper/2306.11731/) (2023)
- [DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting](https://mlsanity.com/paper/2405.00522/) (2024)
- [Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarking](https://mlsanity.com/paper/2404.07533/) (2024)
- [Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens](https://mlsanity.com/paper/2303.17031/) (2023)
- [What Determines the Price of NFTs?](https://mlsanity.com/paper/2310.01815/) (2023)
- [Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols](https://mlsanity.com/paper/2604.20374/) (2026)

## Abstract (arXiv)

Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this profitable market. However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, we propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts. A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.