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arXiv · 2302.03525

Multi-Task Deep Recommender Systems: A Survey

Yuhao Wang·Ha Tsz Lam·Yi Wong·Ziru Liu·Wanyu Wang·Maolin Wang·Yichao Wang·Bo Chen·Huifeng Guo·Ruiming Tang·Xiangyu Zhao
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Abstract

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.

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Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Wanyu Wang, Maolin Wang, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang, Xiangyu Zhao. 2023-02-07. Multi-Task Deep Recommender Systems: A Survey. https://arxiv.org/abs/2302.03525

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