  Click-through rate (CTR) Prediction is of great importance in real-world
online ads systems. One challenge for the CTR prediction task is to capture the
real interest of users from their clicked items, which is inherently biased by
presented positions of items, i.e., more front positions tend to obtain higher
CTR values. A popular line of existing works focuses on explicitly estimating
position bias by result randomization which is expensive and inefficient, or by
inverse propensity weighting (IPW) which relies heavily on the quality of the
propensity estimation. Another common solution is modeling position as features
during offline training and simply adopting fixed value or dropout tricks when
serving. However, training-inference inconsistency can lead to sub-optimal
performance. Furthermore, post-click information such as position values is
informative while less exploited in CTR prediction. This work proposes a simple
yet efficient knowledge distillation framework to alleviate the impact of
position bias and leverage position information to improve CTR prediction. We
demonstrate the performance of our proposed method on a real-world production
dataset and online A/B tests, achieving significant improvements over competing
baseline models. The proposed method has been deployed in the real world online
ads systems, serving main traffic on one of the world's largest e-commercial
platforms.
