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.
翻译: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模型的培训取决于点击数据,这种数据在本质上偏向于较高职位,因为较高职位具有较高的CTR性质。现有的方法,如具有固定职位推断力的实际职位培训,以及没有位置推断力的反向偏差加权培训等,可以将偏差问题减轻到一定程度。然而,培训与推断之间对职位信息的不同处理,将不可避免地导致在网上工作上的不一致和次优业绩。同时,这些方法的基本假设,即点击概率是检查概率和相关性概率的产物,过于简单,不足以模拟职位和其他信息之间的丰富互动。在本文件中,我们提议建立一个深定位点对定位的交互式互动网络(DPIN),以便有效地将所有候选项目和职位结合起来,在离线与在线之间实现一致性,以及模拟职位、用户、背景和业绩限制下的深度非线性互动。在CTR预测中,我们的新处理位置的概率和相关性概率和相关性概率的概率的产物。在CTRA预测中,我们提出的一个衡量我们所部署的准确的准确的、衡量世界等级的方法,一个衡量我们所测定的新的标准。