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：多任务学习旨在统一模型中学习相关任务，通过任务间的知识共享实现相互促进。由于推荐系统对多任务预测的性能与效率需求，该领域已成为推荐方向的重要研究课题。尽管多任务学习已获得充分研究与发展，但推荐领域仍缺乏系统性综述。为弥补这一空白，本文全面综述了现有多任务深度推荐系统。具体而言，首先给出多任务深度推荐系统的问题定义，并将其与其他相关领域进行对比；其次，阐述该领域的发展历程，从任务关系和方法论两个维度引入分类体系——任务关系分为并行型、级联型、辅助主任务型，方法论则分为参数共享、优化策略与训练机制三大类。最后总结多任务深度推荐系统的应用场景与公开数据集，并指出该领域的挑战与未来研究方向。