Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.

翻译：多任务学习（MTL）旨在通过统一模型学习相关任务，利用任务间的共享知识实现相互提升。由于推荐系统需要兼顾性能与效率的多任务预测，MTL已成为推荐领域的重要课题。尽管MTL已得到充分研究和开发，但推荐社区仍缺乏系统性综述。为填补这一空白，本文对现有多任务深度推荐系统（MTDRS）进行了全面综述。具体而言，首先给出MTDRS的问题定义，并将其与相关领域进行对比。其次，描绘MTDRS的发展历程，并从任务关系和方法论两方面引入分类体系。其中，任务关系分为并行、级联、辅助与主任务三类，方法论则归纳为参数共享、优化与训练机制三类。最后，本文总结了MTDRS的应用场景与公开数据集，并指出该领域的挑战与未来方向。