论文标题
数字资产评估：有关域名，电子邮件地址和NFTS的研究
Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTs
论文作者
论文摘要
在互联网上评估数字资产的现有作品通常集中于单个资产类别。为了促进自动化估值技术的开发，最好是通常适用于多个资产类别的估值技术，我们构建了Dash，这是第一个具有从经典到基于区块链的数字资产类别的数字资产销售历史记录数据集。 Consisting of 280K transactions of domain names (DASH_DN), email addresses (DASH_EA), and non-fungible token (NFT)-based identifiers (DASH_NFT), such as Ethereum Name Service names, DASH advances the field in several aspects: the subsets DASH_DN, DASH_EA, and DASH_NFT are the largest freely accessible domain name transaction dataset, the only publicly可用的电子邮件地址交易数据集和第一个NFT事务数据集分别集中在标识符上。 我们建立了强大的基于传统功能的模型作为破折号的基线。接下来，我们将探索基于微调预培训的语言模型的深度学习模型，这些模型尚未在以前的文献中探讨数字资产评估。我们发现，香草微调模型的性能已经很好，表现优于表现最佳的基线。我们进一步提出改进，以使模型更加了解交易的时间敏感性和资产的普及。实验结果表明，我们改进的模型始终优于仪表板所有资产类别的所有其他模型。
Existing works on valuing digital assets on the Internet typically focus on a single asset class. To promote the development of automated valuation techniques, preferably those that are generally applicable to multiple asset classes, we construct DASH, the first Digital Asset Sales History dataset that features multiple digital asset classes spanning from classical to blockchain-based ones. Consisting of 280K transactions of domain names (DASH_DN), email addresses (DASH_EA), and non-fungible token (NFT)-based identifiers (DASH_NFT), such as Ethereum Name Service names, DASH advances the field in several aspects: the subsets DASH_DN, DASH_EA, and DASH_NFT are the largest freely accessible domain name transaction dataset, the only publicly available email address transaction dataset, and the first NFT transaction dataset that focuses on identifiers, respectively. We build strong conventional feature-based models as the baselines for DASH. We next explore deep learning models based on fine-tuning pre-trained language models, which have not yet been explored for digital asset valuation in the previous literature. We find that the vanilla fine-tuned model already performs reasonably well, outperforming all but the best-performing baselines. We further propose improvements to make the model more aware of the time sensitivity of transactions and the popularity of assets. Experimental results show that our improved model consistently outperforms all the other models across all asset classes on DASH.
