On-device machine learning enables the lightweight deployment of
recommendation models in local clients, which reduces the burden of the
cloud-based recommenders and simultaneously incorporates more real-time user
features. Nevertheless, the cloud-based recommendation in the industry is still
very important considering its powerful model capacity and the efficient
candidate generation from the billion-scale item pool. Previous attempts to
integrate the merits of both paradigms mainly resort to a sequential mechanism,
which builds the on-device recommender on top of the cloud-based
recommendation. However, such a design is inflexible when user interests
dramatically change:
the on-device model is stuck by the limited item cache while the cloud-based
recommendation based on the large item pool do not respond without the new
re-fresh feedback.
To overcome this issue, we propose a meta controller to dynamically manage
the collaboration between the on-device recommender and the cloud-based
recommender, and introduce a novel efficient sample construction from the
causal perspective to solve the dataset absence issue of meta controller. On
the basis of the counterfactual samples and the extended training, extensive
experiments in the industrial recommendation scenarios show the promise of meta
controller in the device-cloud collaboration.

本研究提出一种基于 meta 控制器和因果透视的样本构建方法以解决设备层和云端层之间协同推荐的问题，并在工业推荐场景中展示了 meta 控制器的潜力。