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.

基于神经网络的多任务学习在推荐系统中取得了显著的改进，本文针对多任务学习中的负迁移问题，提出了一种名为DTRN的深度任务特定底层表示网络，通过参数高效的超网络，显式地获得每个任务的特定底层表示，并通过类似SENet的网络对特征表示进行改进，实现了任务之间的解耦和互不干扰。实验证明了DTRN的有效性，并在工业推荐系统中获得了显著的改进。

深度针对任务的底层表示网络用于多任务推荐