Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture. However, the MoE-based method, which aims to project all information in the same feature space, cannot effectively deal with the complex relationships inherent among various scenarios and tasks, resulting in unsatisfactory performance. To tackle the problem, we propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation, which achieves hierarchical extraction based on coarse-to-fine knowledge transfer scheme. The multiple extraction layers of the hierarchical network enable the model to enhance the capability of transferring valuable information across scenarios while preserving specific features of scenarios and tasks. Furthermore, a novel scenario-aware attentive network module is proposed to model correlations between scenarios explicitly. Comprehensive experiments conducted on real-world industrial datasets from Meituan Meishi platform demonstrate that HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions. HiNet is currently fully deployed in two scenarios and has achieved 2.87% and 1.75% order quantity gain respectively.

翻译：多场景多任务学习已广泛应用于工业应用中的诸多推荐系统，其中一种有效且实用的方法是在混合专家（Mixture-of-Expert, MoE）架构基础上进行多场景迁移学习。然而，基于MoE的方法旨在将全部信息投射到同一特征空间中，无法有效处理不同场景与任务之间固有的复杂关联，从而导致性能不尽如人意。针对这一问题，我们提出了一种用于多场景多任务推荐的层次化信息提取网络（HiNet），该网络基于从粗粒度到细粒度的知识迁移方案实现层次化信息提取。层次化网络的多个提取层使模型能够增强跨场景传递有价值信息的能力，同时保留场景和任务的特定特征。此外，我们提出了一种新颖的场景感知注意力网络模块，用于显式建模场景之间的相关性。在美团美食平台真实工业数据集上进行的综合实验表明，HiNet达到了新的最优性能，并显著优于现有解决方案。目前，HiNet已在两个场景中全面部署，分别实现了2.87%和1.75%的订单量增长。