报告题目：Reveal the Value of Data: A POC Pricing Mechanism for Data Trading Platform
报告人：林绍波
讲座时间：2026年10月25日 9:00
地点：综合楼644会议室
报告人简介：
林绍波，西安交通大学管理学院，教授、博士生导师。研究方向为分布式学习理论、深度学习理论及强化学习理论。在JMLR，TPAMI，TIT,IJOC等期刊发表学术论文70余篇。主持或以核心骨干参与国家级课题11项。
报告摘要：
Currently, data trading platforms are confronted with three major challenges: inadequate pre-purchase utility assessment, opaque pricing mechanisms, and imperfect after-sales support. These issues result in severe information asymmetry between buyers and sellers, thereby undermining market efficiency. To address these problems, we have proposed a data trading platform that incorporates a Kernel-based Bidirectional Evaluation Assessment Mechanism (KBEAM). At its core lies the Multilevel Regularization Kernel Method (MRKM), which enables accurate value assessment using only a small number of free samples (2%-5% for general predictive tasks and approximately 10% for extreme event detection). This significantly reduces the cost of information disclosure for sellers. The platform offers comprehensive oversight at different transaction stages. Before the sale, it monitors versioning strategies to ensure sample representativeness. During the sale, it provides value - based search and purchase recommendations. After the sale, it offers a data enhancement compensation mechanism. Through extensive experiments on datasets from multiple fields, we have found that the dynamic pricing of KBEAM outperforms traditional strategies in various market scenarios, maximizing both transaction rates and revenues. Moreover, the after-sales mechanism incentivizes both parties to participate honestly. This research provides practical guidelines for determining the quantity of free samples, selecting pricing methods, and optimizing after - sales services on data platforms. It effectively bridges the functional gap between data and physical goods trading platforms, offering a blueprint for constructing reliable data trading platforms and promoting the development of the data economy.
