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 have 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.

翻译：基于神经网络的多任务学习（MTL）已取得显著进展，并被成功应用于推荐系统（RS）。近期针对RS的深度MTL方法（如MMoE、PLE）专注于设计基于软门控的参数共享网络，隐式地学习每个任务的通用表示。然而，当处理冲突任务时，MTL方法可能面临性能退化，因为任务共享的底层表示可能产生负迁移效应。这导致MTL方法捕获任务特定特征的能力下降，最终阻碍其有效性并影响所有任务上的泛化能力。本文聚焦于RS中MTL的底层表示学习，提出深度任务特定底层表示网络（DTRN）以缓解负迁移问题。DTRN通过在底层表示建模阶段为每个任务设置独立的表示学习网络，显式获取任务特定的底层表示。具体而言，它通过参数高效超网络从多种类型的行为序列中为每个任务提取用户兴趣。为进一步获得每个任务的专属表示，DTRN采用类似SENet的网络为每个任务细化各特征的表示。这两个模块可实现获取任务特定底层表示、缓解任务间相互干扰的目标。此外，所提DTRN可灵活地与现有MTL方法结合。在公开数据集和工业数据集上的实验证明了DTRN的有效性。