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}.

翻译：基于多任务学习（MTL）的推荐系统算法是互联网运营商在多行为平台场景中理解用户并预测其行为的主要方法。任务相关性是MTL目标的重要考量，传统模型采用共享底层模型和门控专家机制来实现共享表征学习与信息差异化。然而，实际任务间的关系往往比现有方法所能处理的共享信息更为复杂。本文提出一种差异化表达并行异构网络（DEPHN）来同时建模多个任务。DEPHN通过采用不同的特征交互方式构建模型底层的专家模块，以提升共享信息流的泛化能力。针对模型对不同任务信息流的区分能力，DEPHN在训练过程中利用特征显式映射和虚拟梯度系数实现专家门控机制，通过考虑门控值差异与任务相关性自适应调整门控单元的学习强度。在人工数据集与真实数据集上的大量实验表明，所提方法能够捕获复杂场景下的任务相关性，并在性能上优于基线模型（已被IJCNN2023录用）。