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.

中文翻译：

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用于 CTR 预测的深度位置交互网络

点击率 (CTR) 预测在在线广告和推荐系统中起着重要作用。在实践中，CTR 模型的训练取决于点击数据，这些数据本质上偏向于更高的位置，因为更高的位置本质上具有更高的 CTR。现有的方法，例如具有固定位置推理的实际位置训练和没有位置推理的逆倾向加权训练，在一定程度上缓解了偏差问题。然而，训练和推理之间对位置信息的不同处理将不可避免地导致不一致和次优的在线性能。同时，这些方法的基本假设，即点击概率是检查概率和相关概率的乘积，过于简单，不足以对位置和其他信息之间的丰富交互进行建模。在本文中，我们提出了一个深度位置智能交互网络（DPIN）来有效地组合所有候选项目和位置以估计每个位置的点击率，实现离线和在线之间的一致性以及建模位置之间的深度非线性交互，服务性能限制下的用户、上下文和项目。在我们对 CTR 预测中的位置偏差进行新处理之后，我们提出了一种名为 PAUC（position-wise AUC）的新评估指标，适用于测量给定位置的排名质量。通过对真实世界数据集的大量实验，我们凭经验证明我们的方法在解决位置偏差问题方面既有效又高效。