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:多设想和多任务学习被广泛应用于工业应用中的许多建议系统,其中有效和实用的方法是在混合专家结构(MOE)架构的基础上进行多设想性转移学习,然而,基于教育部的方法旨在在同一特征空间投射所有信息,无法有效地处理各种设想和任务之间固有的复杂关系,导致业绩不尽人意。为了解决这一问题,我们提议建立一个跨设想和多任务建议的等级信息提取网络(HiNet),实现基于全方位至全方位知识转移计划的分级提取。分级网络的多重提取层使该模型能够提高跨情景传输宝贵信息的能力,同时保留情景和任务的具体特征。此外,还提议建立一个新设想性关注网络模块,以明确模拟各种情景和任务之间的关联,对Mietuuan Meishishi平台真实世界工业数据集进行的全面实验表明,HiNet在新状态和显著超标化的知识转移计划基础上实现分级提取。HiNet目前实现了2-87的绩效和显著超标的量化方案。</s>