Neural-based multi-task learning (MTL) has been successfully applied to many recommendation applications. However, these MTL models (e.g., MMoE, PLE) did not consider feature interaction during the optimization, which is crucial for capturing complex high-order features and has been widely used in ranking models for real-world recommender systems. Moreover, through feature importance analysis across various tasks in MTL, we have observed an interesting divergence phenomenon that the same feature can have significantly different importance across different tasks in MTL. To address these issues, we propose Deep Multiple Task-specific Feature Interactions Network (DTN) with a novel model structure design. DTN introduces multiple diversified task-specific feature interaction methods and task-sensitive network in MTL networks, enabling the model to learn task-specific diversified feature interaction representations, which improves the efficiency of joint representation learning in a general setup. We applied DTN to our company's real-world E-commerce recommendation dataset, which consisted of over 6.3 billion samples, the results demonstrated that DTN significantly outperformed state-of-the-art MTL models. Moreover, during online evaluation of DTN in a large-scale E-commerce recommender system, we observed a 3.28% in clicks, a 3.10% increase in orders and a 2.70% increase in GMV (Gross Merchandise Value) compared to the state-of-the-art MTL models. Finally, extensive offline experiments conducted on public benchmark datasets demonstrate that DTN can be applied to various scenarios beyond recommendations, enhancing the performance of ranking models.

翻译：基于神经网络的多任务学习（MTL）已成功应用于众多推荐场景。然而，现有MTL模型（如MMoE、PLE）在优化过程中未考虑特征交互，而特征交互对于捕捉复杂的高阶特征至关重要，并已在现实世界推荐系统的排序模型中广泛应用。此外，通过对MTL中不同任务的特征重要性分析，我们观察到一个有趣的分歧现象：同一特征在MTL的不同任务中可能具有显著不同的重要性。为解决这些问题，我们提出了一种具有新颖模型结构设计的深度多任务特定特征交互网络（DTN）。DTN在MTL网络中引入了多种多样化的任务特定特征交互方法与任务敏感网络，使模型能够学习任务特定的多样化特征交互表示，从而提升通用设置下联合表示学习的效率。我们将DTN应用于公司真实的电子商务推荐数据集（包含超过63亿样本），结果表明DTN显著优于当前最先进的MTL模型。此外，在大规模电子商务推荐系统中对DTN进行在线评估时，与现有最优MTL模型相比，我们观察到点击率提升3.28%、订单量增长3.10%、商品交易总额（GMV）提高2.70%。最后，在公共基准数据集上进行的大量离线实验证明，DTN可应用于推荐之外的多种场景，有效提升排序模型的性能。