Hardness-aware Privileged Features Distillation with Latent Alignment for CVR Prediction

In computational advertising, predicting the post-click conversion rate (CVR) using deep neural networks (DNNs) benefits a lot from privileged features, which can be collected for offline training but are unavailable during online serving.To utilize these privileged signals, privileged features distillation (PFD) methods incorporate a teacher model with privileged features to guide the CVR model.However, existing PFD approaches fail to put more emphasis on poorly predicted instances where the teacher's guidance is most crucial, and thus suffer from overconfidence on "easy" instances.In this work, we propose Hardness-aware Privileged Features Distillation (HA-PFD) for enhancing CVR prediction in real-world advertising recommender systems.We specifically design focal-style distillation losses that adaptively adjust the weight of each instance based on its "hardness".This method prioritizes poorly predicted instances during the distillation process, resulting in improved ranking performance and better model calibration.Additionally, we incorporate latent-level distillation into the PFD framework for the first time, which facilitates the student's representation learning through a straightforward layer alignment approach.We also propose a method for selecting privileged features based on their relevance to the conversion label.We conduct extensive offline experiments on large-scale, real-world datasets and online experiments on Douyin, a short video platform with billions of live users.In the offline evaluation, HA-PFD exhibits competitive performance and superior model calibration compared to existing state-of-the-art methods.In the online experiments, HA-PFD significantly improves advertiser value and conversions.Now we have deployed HA-PFD as the main online serving model on our short video platform.