Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and
retrieval systems in the industry because it facilitates transfer learning from
different scenarios, mitigating data sparsity and reducing maintenance cost.
These efforts produce different MSL paradigms by searching more optimal network
structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network.
It is intuitive that different scenarios could hold their specific
characteristics, activating the user's intents quite differently. In other
words, different kinds of auxiliary features would bear varying importance
under different scenarios. With more discriminative feature representations
refined in a scenario-aware manner, better ranking performance could be easily
obtained without expensive search for the optimal network structure.
Unfortunately, this simple idea is mainly overlooked but much desired in
real-world systems.Further analysis also validates the rationality of adaptive
feature learning under a multi-scenario scheme. Moreover, our A/B test results
on the Alibaba search advertising platform also demonstrate that Maria is
superior in production environments.

Multi-Scenario Learning is used in recommendation and retrieval systems to transfer learning from different scenarios via different paradigms to refine feature representations in a scenario-aware manner, demonstrating superior ranking performance and adaptive feature learning in production environments, as supported by A/B test results on the Alibaba search advertising platform.

自适应特征学习的多场景排名

Multi-Scenario Ranking with Adaptive Feature Learning

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.

提出了一种 HiNet 多场景和多任务推荐系统的层次信息提取网络，通过粗到细的知识转移方案实现层次信息提取，并引入了新颖的场景感知的注意力网络模块来明确场景之间的相关性，实验表明提出的方法在真实世界的产业环境中具有新的最先进的性能并显著优于现有解决方案。

HiNet: 多场景多任务层级信息抽取的新型多任务学习技术

HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction

Multi-scenario learning (MSL) enables a service provider to cater for users'
fine-grained demands by separating services for different user sectors, e.g.,
by user's geographical region. Under each scenario there is a need to optimize
multiple task-specific targets e.g., click through rate and conversion rate,
known as multi-task learning (MTL). Recent solutions for MSL and MTL are mostly
based on the multi-gate mixture-of-experts (MMoE) architecture. MMoE structure
is typically static and its design requires domain-specific knowledge, making
it less effective in handling both MSL and MTL. In this paper, we propose a
novel Automatic Expert Selection framework for Multi-scenario and Multi-task
search, named AESM^{2}. AESM^{2} integrates both MSL and MTL into a unified
framework with an automatic structure learning. Specifically, AESM^{2} stacks
multi-task layers over multi-scenario layers. This hierarchical design enables
us to flexibly establish intrinsic connections between different scenarios, and
at the same time also supports high-level feature extraction for different
tasks. At each multi-scenario/multi-task layer, a novel expert selection
algorithm is proposed to automatically identify scenario-/task-specific and
shared experts for each input. Experiments over two real-world large-scale
datasets demonstrate the effectiveness of AESM^{2} over a battery of strong
baselines. Online A/B test also shows substantial performance gain on multiple
metrics. Currently, AESM^{2} has been deployed online for serving major
traffic.

该论文提出了一种名为 AESM^{2} 的新型自动专家选择框架，该框架将多场景学习（MSL）和多任务学习（MTL）整合到一个统一的框架中，具有自动结构学习的特点，并采用分层设计和专家选择算法，实验表明在真实世界的大规模数据集上表现良好且有助于提高多项指标的在线 A/B 测试。