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：摘要：多场景与多任务学习已广泛应用于工业推荐系统中，其中基于混合专家（MoE）架构进行多场景迁移学习是一种有效且实用的方法。然而，基于MoE的方法将所有信息投影到同一特征空间，无法有效处理不同场景与任务间的复杂关联关系，导致性能欠佳。针对这一问题，我们提出了一种面向多场景多任务推荐的层次化信息提取网络（HiNet），该方法基于从粗到细的知识迁移机制实现层次化提取。层次化网络的多个提取层使模型在保留场景与任务特有特征的同时，增强了跨场景传递有价值信息的能力。此外，本文创新性地提出了场景感知注意力网络模块，用于显式建模场景间的关联关系。在美团美食平台真实工业数据集上的综合实验表明，HiNet达到了新的业界最优性能，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%的订单量提升。