向我展示您的NFT，我会告诉您它的性能：用于NFT销售价格预测的多模态表示学习
Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction
摘要：
不可替代代币（NFT）代表基于区块链技术和智能合约的数字艺术形式（例如，艺术品或收藏品）。在2021年暴涨之后，NFT吸引了加密爱好者和投资者的注意，他们打算在这个有利可图的市场上进行有前途的投资。然而，NFT的财务业绩预测尚未得到广泛的探索。在这项工作中，我们解决了上述问题的基础上的假设，NFT图像和它们的文字描述是必不可少的代理预测NFT销售价格。为此，我们提出了MERLIN，这是一种新型的多模态深度学习框架，旨在训练基于Transformer的语言和视觉模型，以及图神经网络模型，用于收集NFT的图像和文本。MERLIN的一个关键方面是它对金融特征的独立性，因为它只利用对NFT交易感兴趣的用户想要处理的主要数据，即，NFT图像和文本描述。通过学习这些数据的密集表示，价格类别分类任务由MERLIN模型执行，该模型还可以根据推理阶段的用户偏好进行调整，以模拟不同的风险回报投资概况。对公开数据集的实验评估表明，MERLIN模型根据几个财务评估标准实现了显著的性能，促进了有利可图的投资，并击败了基于财务特征的基线机器学习分类器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
