Adaptive Structure Learning with Partial Parameter Sharing for Post-Click Conversion Rate Prediction

The post-click conversion rate (CVR) prediction task aims to predict the probability of a conversion after a click, which is essential in many fields. There are two widely-recognized challenges for CVR prediction: selection bias and data sparsity. Many previous methods focus on addressing selection bias by unbiasedly estimating the ideal loss based on the doubly robust estimator, which incorporates the error imputation model and propensity model to help CVR prediction model learning. However, they struggle with unreasonable knowledge transfer between the prediction model and imputation model and inflexible network structure design under sparse data. To this end, we introduce a novel principled adaptive structure learning approach, named Adap-SL, to adaptively learn the optimal network structure, adjust the number of activated (non-zero) parameters, and determine which knowledge needs to be transferred between the prediction model and the imputation model. Specifically, we start with an over-parameterized base network, where we adaptively extract partially overlapped subnetworks for the imputation model and the prediction model. Extensive experiments are conducted on three real-world recommendation datasets, demonstrating that our method consistently improves performance while requiring fewer parameters. The code is available at https://github.com/ChunyuanZheng/sigir25-sparse-sharing.