Recommendation system algorithm based on multi-task learning (MTL) is the major method for Internet operators to understand users and predict their behaviors in the multi-behavior scenario of platform. Task correlation is an important consideration of MTL goals, traditional models use shared-bottom models and gating experts to realize shared representation learning and information differentiation. However, The relationship between real-world tasks is often more complex than existing methods do not handle properly sharing information. In this paper, we propose an Different Expression Parallel Heterogeneous Network (DEPHN) to model multiple tasks simultaneously. DEPHN constructs the experts at the bottom of the model by using different feature interaction methods to improve the generalization ability of the shared information flow. In view of the model's differentiating ability for different task information flows, DEPHN uses feature explicit mapping and virtual gradient coefficient for expert gating during the training process, and adaptively adjusts the learning intensity of the gated unit by considering the difference of gating values and task correlation. Extensive experiments on artificial and real-world datasets demonstrate that our proposed method can capture task correlation in complex situations and achieve better performance than baseline models\footnote{Accepted in IJCNN2023}.

基于多任务学习的推荐系统算法是互联网运营商在平台多行为场景中了解用户并预测其行为的主要方法，然而现有方法未能妥善处理真实世界任务之间更为复杂的关系，本文提出了一种不同表达的并行异构网络（DEPHN），以同时建模多个任务。DEPHN通过使用不同的特征交互方法构建模型底部的专家来提高共享信息流的泛化能力，在训练过程中使用特征显式映射和虚拟梯度系数进行专家门控，通过考虑门控值的差异和任务相关性来自适应地调整门控单元的学习强度。人工和真实世界数据集上的大量实验证明了我们提出的方法能够在复杂情况下捕捉任务相关性，并且相比基准模型表现更好。

DEPHN: 基于虚拟梯度优化的不同表达并行异构网络，用于多任务学习