How to reverse-engineer quality rankings
How to reverse-engineer quality rankings
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DOI：
10.1007/s10994-012-5295-6
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发表时间：
2012-09-01
期刊：
影响因子：
7.5
通讯作者：
Chou, Gloria
中科院分区：
文献类型：
--
作者：
Chang, Allison;Rudin, Cynthia;Chou, Gloria
一个好的或坏的产品质量评级可以使或打破一个组织。然而，"质量"的概念往往是由一家独立的评级公司界定的，该公司不公开确定产品等级的公式。为了明智地投资于产品开发，组织开始使用智能方法来确定产品开发的资金应该如何分配。这一过程的关键一步是尽可能地“逆向工程”评级公司的专有模型。在这项工作中，我们为这项任务提供了一种机器学习方法，该方法优化了特定的排名统计，该统计对特定于质量评级数据的偏好信息进行编码。我们目前的实验数据从一个主要的质量评级公司，并提供新的方法来评估解决方案。此外，我们提供了一种方法来使用逆向工程模型，以实现一个具有成本效益的方式排名靠前的产品。
A good or bad product quality rating can make or break an organization. However, the notion of "quality" is often defined by an independent rating company that does not make the formula for determining the rank of a product publicly available. In order to invest wisely in product development, organizations are starting to use intelligent approaches for determining how funding for product development should be allocated. A critical step in this process is to "reverse-engineer" a rating company's proprietary model as closely as possible. In this work, we provide a machine learning approach for this task, which optimizes a certain rank statistic that encodes preference information specific to quality rating data. We present experiments on data from a major quality rating company, and provide new methods for evaluating the solution. In addition, we provide an approach to use the reverse-engineered model to achieve a top ranked product in a cost-effective way.