论文标题
从优化参与到衡量价值
From Optimizing Engagement to Measuring Value
论文作者
论文摘要
当今大多数建议引擎基于预测用户参与度，例如预测用户是否会单击项目。但是，参与信号与值得优化的“价值”概念之间存在很大的差距。我们使用测量理论的框架来（a）面对设计人员，面对一个规范性问题，即设计器值是什么，（b）提供了一种通用的潜在变量模型方法，该方法可用于操作目标结构并直接为其进行优化，以及（c）指导设计师评估和修订其操作。我们在Twitter平台上对数百万用户实施我们的方法。根据评估测量的有效性的既定方法，我们对模型捕获所需的“价值”概念的方式进行定性评估。
Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap between engagement signals and a desired notion of "value" that is worth optimizing for. We use the framework of measurement theory to (a) confront the designer with a normative question about what the designer values, (b) provide a general latent variable model approach that can be used to operationalize the target construct and directly optimize for it, and (c) guide the designer in evaluating and revising their operationalization. We implement our approach on the Twitter platform on millions of users. In line with established approaches to assessing the validity of measurements, we perform a qualitative evaluation of how well our model captures a desired notion of "value".
