发表机构
ZJU（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数据产品定价难题，引入含全球9大数据市场16147个数据产品元数据的DaDaDa数据集，用于训练定价模型、建立价格基准及数据产品分类与检索等，实验证明其在数据产品定价、分类和检索方面有效。
AI 中文摘要
高质量数据推动各行业机器学习发展。数据交易渐趋普遍，数据市场不断涌现，但数据产品定价仍是难题。传统经济学定价方法在数据定价中存在局限。为此，我们引入DaDaDa，首个数据产品定价数据集，含全球9大数据市场16147个数据产品元数据。它能训练定价模型，建立价格基准，还可用于数据产品分类与检索等任务。实验及检索原型证明了其有效性，数据集和代码可获取。
英文摘要
High-quality data drives machine learning advances across industries. Recognizing the value of data, data transactions are increasingly common, giving rise to many data marketplaces, e.g., AWS Marketplace, Databricks, and Datarade. However, determining the appropriate prices for data products remains a significant challenge due to the unique properties of data products. Traditional pricing methods in economics can be categorized into the cost approach, the income approach, and the sales comparison approach. The cost approach fails in data pricing due to near-zero marginal cost from data replication, and the income approach fails due to inherently unpredictable data revenue. The sales comparison approach remains viable, yet its application is hindered by the absence of standardized pricing benchmarks for data products across marketplaces. To address this challenge, we introduce \texttt{DaDaDa}, the first dataset for data product pricing, containing metadata for 16,147 data products from 9 major data marketplaces worldwide. \texttt{DaDaDa} enables the training of pricing models, thereby establishing price benchmarks for new data products. In addition, \texttt{DaDaDa} can be utilized for other important tasks in data markets, such as data product classification and retrieval. Experiments and a retrieval prototype demonstrate the effectiveness of \texttt{DaDaDa} for pricing, classification, and retrieval of data products. The dataset and code are available at https://github.com/ZJU-DIVER/DaDaDa.
