  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.
