Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.

翻译：级联排序广泛用于在线广告和推荐系统中的大规模top-k选择问题，而学习排序是优化级联排序模型的重要方式。以往的学习排序研究通常聚焦于让模型学习完整排序或top-k排序，并采用相应的排序指标（如OPA和NDCG@k）作为优化目标。然而，这些目标无法适应数据复杂度和模型能力各异的多种级联排序场景；同时，现有基于指标驱动的方法（如Lambda框架）只能优化有限指标的粗略上界，可能导致次优结果和性能偏差。针对这些问题，我们提出优化级联排序系统的新视角，突出优化目标对数据复杂度和模型能力的适应性。具体而言，我们采用多任务学习自适应地结合松弛目标与完整目标的优化，分别对应Recall@m@k指标和OPA指标。我们还引入排列矩阵表示排序指标，并利用可微排序技术松弛硬排列矩阵，且具有可控的近似误差界。这使得我们能够更直接且适当地同时优化松弛目标和完整目标。我们将该方法命名为自适应神经排序框架（简称ARF）。此外，我们给出了ARF框架下的具体实践：采用NeuralSort获取松弛排列矩阵，并借鉴多任务学习中不确定性权重方法的变体来联合优化所提出的损失函数。在4个公共和工业基准数据集上的实验验证了该方法的有效性和泛化能力，在线实验表明该方法具有显著的应用价值。