Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based parameter-sharing networks that implicitly learn a generalized representation for each task. However, MTL methods may suffer from performance degeneration when dealing with conflicting tasks, as negative transfer effects can occur on the task-shared bottom representation. This can result in a reduced capacity for MTL methods to capture task-specific characteristics, ultimately impeding their effectiveness and hindering the ability to generalize well on all tasks. In this paper, we focus on the bottom representation learning of MTL in RS and propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem. DTRN obtains task-specific bottom representation explicitly by making each task has its own representation learning network in the bottom representation modeling stage. Specifically, it extracts the user's interests from multiple types of behavior sequences for each task through the parameter-efficient hypernetwork. To further obtain the dedicated representation for each task, DTRN refines the representation of each feature by employing a SENet-like network for each task. The two proposed modules can achieve the purpose of getting task-specific bottom representation to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible to combine with existing MTL methods. Experiments on one public dataset and one industrial dataset demonstrate the effectiveness of the proposed DTRN. Furthermore, we deploy DTRN in an industrial recommender system and gain remarkable improvements in multiple tasks.

翻译：基于神经网络的多任务学习取得了显著进展，并已成功应用于推荐系统。近期针对推荐系统的深度多任务学习方法（如MMoE、PLE）聚焦于设计基于软门控的参数共享网络，隐式地为每个任务学习通用表示。然而，当处理冲突任务时，多任务学习方法可能面临性能退化问题，因为任务共享的底层表示上可能产生负迁移效应。这会导致多任务学习方法捕获任务特定特征的能力降低，最终阻碍其有效性并削弱在所有任务上的泛化能力。本文聚焦于推荐系统中多任务学习的底层表示学习，提出深度任务特定底层表示网络来缓解负迁移问题。DTRN通过使每个任务在底层表示建模阶段拥有自身的表示学习网络，显式地获取任务特定底层表示。具体而言，它通过参数高效的超网络从每个任务的多类型行为序列中提取用户兴趣。为进一步获得每个任务的专属表示，DTRN采用类似SENet的网络对每个特征进行精炼，为每个任务定制化表示。这两个提出的模块能够实现获取任务特定底层表示的目的，从而缓解任务间的相互干扰。此外，所提出的DTRN可以灵活地与现有多任务学习方法结合。在公开数据集和工业数据集上的实验证明了所提DTRN的有效性。此外，我们将DTRN部署到工业推荐系统中，在多个任务上取得了显著提升。