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arXiv · 2304.04959

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Danwei Li·Zhengyu Zhang·Siyang Yuan·Mingze Gao·Weilin Zhang·Chaofei Yang·Xi Liu·Jiyan Yang
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Abstract

Multi-task learning (MTL) aims to enhance the performance and efficiency of machine learning models by simultaneously training them on multiple tasks. However, MTL research faces two challenges: 1) effectively modeling the relationships between tasks to enable knowledge sharing, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model called Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task-specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and a gating mechanism for task-to-task fusion, these units adaptively learn both shared knowledge and task-specific knowledge. To evaluate AdaTT's performance, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT significantly outperforms existing state-of-the-art baselines. Furthermore, our end-to-end experiments reveal that the model exhibits better performance compared to alternatives.

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Danwei Li, Zhengyu Zhang, Siyang Yuan, Mingze Gao, Weilin Zhang, Chaofei Yang, Xi Liu, Jiyan Yang. 2023-04-11. AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations. https://arxiv.org/abs/2304.04959

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