On-Device Model Fine-Tuning with Label Correction in Recommender Systems. (arXiv:2211.01163v1 [cs.IR])

To meet the practical requirements of low latency, low cost, and good privacy
in online intelligent services, more and more deep learning models are
offloaded from the cloud to mobile devices. To further deal with cross-device
data heterogeneity, the offloaded models normally need to be fine-tuned with
each individual user's local samples before being put into real-time inference.
In this work, we focus on the fundamental click-through rate (CTR) prediction
task in recommender systems and study how to effectively and …

arxiv fine-tuning model fine-tuning recommender systems systems