  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.
