GFlow-FT: Pick a Child Network via Gradient Flow for Efficient Fine-Tuning in Recommendation Systems

Conversion Rate (CVR) prediction is a crucial task in online advertising systems. Existing single-domain CVR prediction models suffer from the data sparsity problem since few users purchase items after clicking. In recent years, a robust and effective technique called fine-tuning can transfer knowledge from a data-rich source domain to enhance the CVR prediction performance in a data-sparse target domain. However, since most CVR prediction models have a large number of parameters, fine-tuning all the parameters on a data-sparse domain may lead to over-fitting. In this paper, we propose a general and efficient transfer learning method called Gradient-Flow based Fine-Tuning (GFlow-FT), which only needs to update a subset of parameters (called child network) via pruning the gradients to restrain gradient norm against over-fitting. In addition, our method employs the gradient-flow based measure via calculating the Hessian-gradient product as the criteria for picking the child network, which is superior to the magnitude-based and loss-based measure from empirical results. Extensive experimental results on three real-world datasets from recommendation systems show that GFlow-FT can significantly improve the performance of CVR prediction compared with state-of-the-art fine-tuning approaches.