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User post-click conversion prediction is of high interest to researchers anddevelopers. Recent studies employ multi-task learning to tackle the selectionbias and data sparsity problem, two severe challenges in post-click behaviorprediction, by incorporating click data. However, prior works mainly focused onpointwise learning and the orders of labels (i.e., click and post-click) arenot well explored, which naturally poses a listwise learning problem. Inspiredby recent advances on differentiable sorting, in this paper, we propose a novelmulti-task framework that leverages orders of user behaviors to predict userpost-click conversion in an end-to-end approach. Specifically, we define anaggregation operator to combine predicted outputs of different tasks to aunified score, then we use the computed scores to model the label relations viadifferentiable sorting. Extensive experiments on public and industrial datasetsshow the superiority of our proposed model against competitive baselines.

Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion