User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by incorporating click data. However, prior works mainly focused on pointwise learning and the orders of labels (i.e., click and post-click) are not well explored, which naturally poses a listwise learning problem. Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. Specifically, we define an aggregation operator to combine predicted outputs of different tasks to a unified score, then we use the computed scores to model the label relations via differentiable sorting. Extensive experiments on public and industrial datasets show the superiority of our proposed model against competitive baselines.

翻译：用户点击后转化预测是研究人员和开发者高度关注的课题。近期研究通过引入点击数据，采用多任务学习来解决点击后行为预测中的两大严峻挑战——选择偏差和数据稀疏问题。然而，现有工作主要关注逐点学习，未充分探索标签（即点击与点击后行为）的顺序关系，而这一问题天然构成了列表级学习任务。受可微排序领域最新进展的启发，本文提出了一种新型多任务框架，通过端到端方式利用用户行为顺序预测点击后转化。具体而言，我们定义了一个聚合算子，将不同任务的预测输出合并为统一分数，随后通过可微排序对这些分数进行标签关系建模。在公开数据集与工业数据集上的大量实验表明，所提模型显著优于强基线方法。