Abstract
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.
Abstract (translated)
多场景和多任务学习已经被广泛应用于工业应用中的许多推荐系统,其中一种有效且实用的方法是基于专家混合(MoE)架构进行多场景迁移学习。然而,基于MoE的方法旨在将所有信息放在相同的特征空间中,无法有效地处理各种场景和任务之间的复杂关系,导致性能不佳。为了解决这个问题,我们提出了一种Hierarchical information extraction Network(HiNet)多场景和多任务推荐,该方法基于细粒度知识传输方案实现Hierarchical提取。HiNet的多个提取层使模型能够增强在不同场景间传输有价值的信息的能力,同时保留场景和任务的特定特征。此外,我们提出了一种名为场景 aware attention network module的新模块,以 explicitly Modeling场景间的关系。从 Meituan Meishi 平台获取的真实工业数据集的 comprehensive 实验表明,HiNet实现了一种新的高性能,并显著优于现有的解决方案。HiNet目前完全部署在两个场景中,并分别实现了2.87%和1.75%的订单量增益。
URL
https://arxiv.org/abs/2303.06095