论文标题
Google Scholar应该更频繁地更新？
Who Should Google Scholar Update More Often?
论文作者
论文摘要
我们考虑了一个受资源约束的更新程序，例如Google Scholar，该更新者希望以一种使总体引用率不同的研究人员的引用记录（以及不同的重要性系数），以使总体引用指数尽可能最新。更新者是资源受限的，无法一直更新所有研究人员的引用。特别是，它需要在单个研究人员之间分配的总更新率约束。我们使用类似于信息时代的度量：根据最新更新，实际引文数和引文数之间的长期平均差异。我们表明，为了最大程度地减少此差异度量，更新者应将其总更新能力分配给研究人员与其平均引文率的$ square $ $ roots $成比例。也就是说，应该更频繁地更新更多多产的研究人员，但是由于平方根函数的凹度，回报率降低。更一般而言，我们的论文解决了资源约束采样器的最佳操作问题，该问题希望以尽可能最新的方式跟踪多个独立的计数过程。
We consider a resource-constrained updater, such as Google Scholar, which wishes to update the citation records of a group of researchers, who have different mean citation rates (and optionally, different importance coefficients), in such a way to keep the overall citation index as up to date as possible. The updater is resource-constrained and cannot update citations of all researchers all the time. In particular, it is subject to a total update rate constraint that it needs to distribute among individual researchers. We use a metric similar to the age of information: the long-term average difference between the actual citation numbers and the citation numbers according to the latest updates. We show that, in order to minimize this difference metric, the updater should allocate its total update capacity to researchers proportional to the $square$ $roots$ of their mean citation rates. That is, more prolific researchers should be updated more often, but there are diminishing returns due to the concavity of the square root function. More generally, our paper addresses the problem of optimal operation of a resource-constrained sampler that wishes to track multiple independent counting processes in a way that is as up to date as possible.
