Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling

Xu, Zhenhui; Zeng, Meng; Liu, Liqun; Zhang, Xiaopeng; Zhang, Bifeng

doi:10.1145/3534678.3539062

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Recently, PFE [71] adapts prototype feature extraction into the MMoE framework. MVKE [72] proposes a virtual kernel structure for expert and gate for better user profiling.…”

Section: Methodsmentioning

confidence: 99%

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Multi-Task Deep Recommender Systems: A Survey

Wang¹,

Lam²,

Wong³

et al. 2023

Preprint

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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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“…Recently, PFE [71] adapts prototype feature extraction into the MMoE framework. MVKE [72] proposes a virtual kernel structure for expert and gate for better user profiling.…”

Section: Methodsmentioning

confidence: 99%

“…MetaHeac [86] is proposed for audience expansion tasks on content based mobile marketing. MVKE [72] is proposed for user tagging for online advertising.…”

Section: Application Fieldsmentioning

confidence: 99%