The industrial recommender systems always pursue more than one business goals. The inherent intensions between objectives pose significant challenges for ranking stage. A popular solution is to build a multi-objective ensemble (ME) model to integrate multi-objective predictions into a unified score. Although there have been some exploratory efforts, few work has yet been able to systematically delineate the core requirements of ME problem. We rethink ME problem from two perspectives. From the perspective of each individual objective, to achieve its maximum value the scores should be as consistent as possible with the ranks of its labels. From the perspective of entire set of objectives, an overall optimum can be achieved only when the scores align with the commonality shared by the majority of objectives. However, none of existing methods can meet these two requirements. To fill this gap, we propose a novel multi-objective ensemble framework HarmonRank to fulfill both requirements. For rank consistency, we formulate rank consistency (AUC) metric as a rank-sum problem and make the model optimized towards rank consistency in an end-to-end differentiable manner. For commonality modeling, we change the original relation-agnostic ensemble paradigm to a relation-aware one. Extensive offline experimental results on two industrial datasets and online experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods. Besides, our method exhibits superior robustness to label skew situations which is common in industrial scenarios. The proposed method has been fully deployed in Kuaishou's live-streaming e-commerce recommendation platform with 400 million DAUs, contributing 2.6% purchase gain.

翻译：工业推荐系统通常追求多个业务目标。目标间的内在冲突为排序阶段带来了重大挑战。一种流行的解决方案是构建多目标集成模型，将多目标预测整合为统一分数。尽管已有一些探索性工作，但鲜有研究能系统性地阐述多目标集成问题的核心需求。我们从两个维度重新审视多目标集成问题：从单个目标视角出发，为实现其价值最大化，预测分数应尽可能与其标签排序保持一致；从整体目标集视角出发，只有当分数与多数目标共享的共性特征对齐时，才能实现全局最优。然而，现有方法均无法同时满足这两项要求。为填补这一空白，我们提出新型多目标集成框架HarmonRank以同时满足双重需求。针对排序一致性，我们将排序一致性指标（AUC）建模为秩和问题，使模型能以端到端可微分方式向排序一致性优化。针对共性建模，我们将原始关系无关的集成范式转变为关系感知范式。在两个工业数据集上的大量离线实验及在线实验表明，我们的方法显著优于现有最先进方法。此外，该方法对工业场景中常见的标签偏斜情况展现出卓越的鲁棒性。所提方法已在快手直播电商推荐平台（日活用户4亿）全面部署，带来2.6%的购买转化率提升。