In this paper, we study multiple problems from sponsored product optimization
in ad system, including position-based de-biasing, click-conversion multi-task
learning, and calibration on predicted click-through-rate (pCTR). We propose a
practical machine learning framework that provides the solutions to such
problems without structural change to existing machine learning models, thus
can be combined with most machine learning models including shallow models
(e.g. gradient boosting decision trees, support vector machines). In this
paper, we first propose data and feature engineering techniques to handle the
aforementioned problems in ad system; after that, we evaluate the benefit of
our practical framework on real-world data sets from our traffic logs from
online shopping site. We show that our proposed practical framework with data
and feature engineering can also handle the perennial problems in ad systems
and bring increments to multiple evaluation metrics.

本文研究赞助产品优化中的多个问题，包括基于位置的去偏差、点击 - 转化多任务学习和预测点击率（pCTR）的校准。我们提出了一个实用的机器学习框架，可以解决这些问题，而不改变现有机器学习模型的结构，并可以与大多数机器学习模型结合使用。我们在真实世界的在线购物网站上评估了我们的提出的实用框架，证明它可以解决广告系统中的固有问题，并为多个评估指标带来增益。