在稀疏数据环境中使用贝叶斯树预测骑手转化

At Lyft, understanding how riders go through our user experience is fundamental to operating a healthy marketplace. Specifically, it is important to have a robust model determining if a rider will actually request a ride after entering a destination and viewing a price and ETA. Accurately predicting this decision, that we call conversion, informs countless decisions across our platform. Whether it is to better balance supply and demand, improve user experiences, optimize recommendations and advertisement, understand long-term engagement, decide how to distribute coupons… rider conversion prediction is a central challenge for the Lyft business.

在 Lyft，理解乘客如何经历我们的用户体验对于运营健康的 marketplace 至关重要。具体来说，拥有一个稳健的模型来确定乘客在输入目的地并查看价格和 ETA 后是否实际会请求乘车非常重要。准确预测这个我们称之为conversion的决策，会影响我们平台上的无数决策。无论是更好地平衡供给和需求、改善用户体验、优化推荐和广告、理解长期参与度、决定如何分发优惠券……乘客转换预测是 Lyft 业务的核心挑战。

However, predicting human behavior at scale is incredibly complex, and the exact same person might well open the app just to check current availability or actually to request a ride after viewing our prices. The contexts under which riders make their conversion decisions are extremely diverse and almost unique to each session. A user’s intent changes based on where they are and where they want to go, what time it is, their previous interactions with the platform, current supply-demand market conditions, to cite a few.

然而，大规模预测人类行为极其复杂，同一人可能只是打开 app 检查当前可用性，或者在查看我们的价格后实际请求 ride。rider 做出 conversion 决定的上下文极其多样，几乎每个 session 都独特。用户的意图会根据他们所在位置、想去的位置、时间、之前与平台的交互、当前的 supply-demand 市场状况等因素而变化。

When we try to model this using standard machine learning approaches, we run into a significant challenge: data sparsity.

当我们尝试使用标准 machine learning 方法来建模时，会遇到一个重大挑战：data sparsity。

The Challenge of High Cardinality and Sparsity

高基数和高稀疏性的挑战

To accurately predict conversion, we need to slice our data very thinly across many categorical features. Imagine trying to predict the conversion probability for a business traveler leaving the suburbs of Detroit at 4:00 AM on a Tuesday to catch their f...