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.

本文提出了一种深度位置交互网络 DPIN，以有效组合所有候选项和位置来估算每个位置的 CTR，实现离线和在线的一致性，并在服务性能的限制下对位置、用户、上下文和项目之间的深度非线性交互进行建模。同时，我们提出了一种评估指标 PAUC (position-wise AUC)，以解决 CTR 预测中的位置偏差问题，并在真实数据集上进行了广泛的实验，证明了方法的有效性和效率。在 A/B 测试中，我们的方法已在生产中部署，并观察到与高度优化的基准相比有显著的改进。