论文标题
没有疑问的调查：加强学习方法
Surveys without Questions: A Reinforcement Learning Approach
论文作者
论文摘要
“旧世界”仪器（调查）仍然是公司在与公司在线互动时获得满意度和经验评级的首选工具。虽然调查途径已从浏览时的电子邮件和链接中发展出来，但缺陷仍然存在。其中包括 - 依靠很少受访者推断所有客户的在线互动的评级；由于评级是一次性快照，因此无法随时间捕获客户的互动。并且无法将客户的评分与特定交互联系起来，因为提供的评分与所有互动有关。为了克服这些缺陷，我们通过开发基于强化学习（RL）的方法来从ClickStream数据中提取ClickStream数据的代理评级，通常是为每个客户的在线互动收集的。我们引入了一种新的方式来解释RL的价值函数产生的值，为代理评级。我们的方法不需要任何调查数据进行培训。但是，在针对实际调查数据的验证后，代理评级会产生合理的绩效结果。此外，我们提供了一种从价值函数的值中获取见解的新方法，这些函数允许将特定的交互与其代理评级相关联。我们介绍了两个新的指标来表示评分 - 一个，客户级别，另一个是跨客户点击操作的汇总级别。两者都是围绕所有成对的，连续的动作的比例，这些动作显示了代理评级的增加。这个直观的客户级指标使得随着时间的推移评分的动力可以比调查的客户评级更好。汇总度量指标允许指出有助于或伤害经验的动作。总而言之，对于每个操作，对于每个客户，每个会话都可以从ClickStream毫不客气地计算出代理评级，并且每个会话都可以为调查提供可解释，更有见地的替代方案。
The 'old world' instrument, survey, remains a tool of choice for firms to obtain ratings of satisfaction and experience that customers realize while interacting online with firms. While avenues for survey have evolved from emails and links to pop-ups while browsing, the deficiencies persist. These include - reliance on ratings of very few respondents to infer about all customers' online interactions; failing to capture a customer's interactions over time since the rating is a one-time snapshot; and inability to tie back customers' ratings to specific interactions because ratings provided relate to all interactions. To overcome these deficiencies we extract proxy ratings from clickstream data, typically collected for every customer's online interactions, by developing an approach based on Reinforcement Learning (RL). We introduce a new way to interpret values generated by the value function of RL, as proxy ratings. Our approach does not need any survey data for training. Yet, on validation against actual survey data, proxy ratings yield reasonable performance results. Additionally, we offer a new way to draw insights from values of the value function, which allow associating specific interactions to their proxy ratings. We introduce two new metrics to represent ratings - one, customer-level and the other, aggregate-level for click actions across customers. Both are defined around proportion of all pairwise, successive actions that show increase in proxy ratings. This intuitive customer-level metric enables gauging the dynamics of ratings over time and is a better predictor of purchase than customer ratings from survey. The aggregate-level metric allows pinpointing actions that help or hurt experience. In sum, proxy ratings computed unobtrusively from clickstream, for every action, for each customer, and for every session can offer interpretable and more insightful alternative to surveys.
