Recommendation for live-streaming e-commerce is gaining increasing attention due to the explosive growth of the live streaming economy. Different from traditional e-commerce, live-streaming e-commerce shifts the focus from products to streamers, which requires ranking mechanism to balance both purchases and user-streamer interactions for long-term ecology. To trade off multiple objectives, a popular solution is to build an ensemble model to integrate multi-objective scores into a unified score. The ensemble model is usually supervised by multiple independent binary classification losses of all objectives. However, this paradigm suffers from two inherent limitations. First, the optimization direction of the binary classification task is misaligned with the ranking task (evaluated by AUC). Second, this paradigm overlooks the alignment between objectives, e.g., comment and buy behaviors are partially dependent which can be revealed in labels correlations. The model can achieve better trade-offs if it learns the aligned parts of ranking abilities among different objectives. To mitigate these limitations, we propose a novel multi-objective ensemble framework HarmonRank to fulfill both alignment to the ranking task and alignment among objectives. For alignment to ranking, we formulate ranking metric AUC as a rank-sum problem and utilize differentiable ranking techniques for ranking-oriented optimization. For inter-objective alignment, we change the original one-step ensemble paradigm to a two-step relation-aware ensemble scheme. Extensive offline experiments results on two industrial datasets and online experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods. The proposed method has been fully deployed in Kuaishou's live-streaming e-commerce recommendation platform with 400 million DAUs, contributing over 2% purchase gain.

翻译：直播电商推荐因直播经济的爆发式增长而日益受到关注。与传统电商不同，直播电商将焦点从商品转向主播，其排序机制需兼顾购买行为与用户-主播互动，以维护长期生态健康。为权衡多个目标，一种主流解决方案是构建集成模型，将多目标得分融合为统一分数。该集成模型通常由各目标的多个独立二元分类损失进行监督。然而，该范式存在两个固有局限：首先，二元分类任务的优化方向与排序任务（以AUC评估）存在偏差；其次，该范式忽视了目标间的内在关联性，例如评论与购买行为存在部分依赖关系，这可通过标签相关性体现。若模型能学习不同目标间排序能力的对齐部分，则可实现更优的权衡。为缓解这些局限，我们提出一种新颖的多目标集成框架HarmonRank，旨在同时实现与排序任务的对齐及目标间的对齐。针对排序对齐，我们将排序指标AUC表述为秩和问题，并利用可微排序技术进行面向排序的优化。针对目标间对齐，我们将原始的一步式集成范式改进为两步式关系感知集成方案。基于两个工业数据集的广泛离线实验及在线实验表明，我们的方法显著优于现有前沿方法。该方案已在日活用户达4亿的快手直播电商推荐平台全面部署，带来超过2%的购买增益。