  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.
