  Click-through rate (CTR) prediction plays an important role in online
advertising and recommender systems. In practice, the training of CTR models
depends on click data which is intrinsically biased towards higher positions
since higher position has higher CTR by nature. Existing methods such as actual
position training with fixed position inference and inverse propensity weighted
training with no position inference alleviate the bias problem to some extend.
However, the different treatment of position information between training and
inference will inevitably lead to inconsistency and sub-optimal online
performance. Meanwhile, the basic assumption of these methods, i.e., the click
probability is the product of examination probability and relevance
probability, is oversimplified and insufficient to model the rich interaction
between position and other information. In this paper, we propose a Deep
Position-wise Interaction Network (DPIN) to efficiently combine all candidate
items and positions for estimating CTR at each position, achieving consistency
between offline and online as well as modeling the deep non-linear interaction
among position, user, context and item under the limit of serving performance.
Following our new treatment to the position bias in CTR prediction, we propose
a new evaluation metrics named PAUC (position-wise AUC) that is suitable for
measuring the ranking quality at a given position. Through extensive
experiments on a real world dataset, we show empirically that our method is
both effective and efficient in solving position bias problem. We have also
deployed our method in production and observed statistically significant
improvement over a highly optimized baseline in a rigorous A/B test.
