在以人为中心的数据经济中评估用户数据
Valuating User Data in a Human-Centric Data Economy
摘要：
为人们的数据付费的想法越来越被视为解决隐私争论、提高在线数据质量，甚至在自动化和自动操作机器主导的未来提供劳动报酬的替代方案的一个有希望的方向。在本文中，我们展示了以人为中心的数据经济将如何补偿在线流媒体服务的用户。我们借用合作博弈论中的Shapley值的概念来定义每个用户应该为推荐系统提供的电影评分提供什么样的公平补偿。由于在一般情况下准确确定Shapley值在计算上是低效的，因此我们使用聚类、降维和部分信息来获得更快的替代方案。我们将我们的算法应用于电影推荐数据集，并证明不同的用户可能对服务有着截然不同的价值。我们还分析了一些电影评级可能比其他电影更有价值的原因，并讨论了公平补偿用户的后果。
英文摘要：
The idea of paying people for their data is increasingly seen as a promising direction for resolving privacy debates, improving the quality of online data, and even offering an alternative to labor-based compensation in a future dominated by automation and self-operating machines. In this paper we demonstrate how a Human-Centric Data Economy would compensate the users of an online streaming service. We borrow the notion of the Shapley value from cooperative game theory to define what a fair compensation for each user should be for movie scores offered to the recommender system of the service. Since determining the Shapley value exactly is computationally inefficient in the general case, we derive faster alternatives using clustering, dimensionality reduction, and partial information. We apply our algorithms to a movie recommendation data set and demonstrate that different users may have a vastly different value for the service. We also analyze the reasons that some movie ratings may be more valuable than others and discuss the consequences for compensating users fairly.
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。
