Feature Missing-aware Routing-and-Fusion Network for Customer Lifetime Value Prediction in Advertising

Nowadays, customer lifetime value (LTV) plays an important role in mobile game advertising, since it can be beneficial to adjust ad bids and ensure that the games are promoted to the most valuable users. Some neural models are utilized for LTV prediction based on the rich user features. However, in the advertising scenario, due to the privacy settings or limited length of log retention, etc, most of existing approaches suffer from the missing feature problem. Moreover, only a small fraction of purchase behaviours can be observed. The label sparsity inevitably limits model expressiveness. To tackle the aforementioned challenges, we propose a feature missing-aware routing-and-fusion network (MarfNet) to reduce the effect of the missing features while training. Specifically, we calculate the missing states of raw features and feature interactions for each sample. Based on the missing states, two missing-aware layers are designed to route samples into different experts, thus each expert can focus on the real features of samples assigned to it. Finally we get the missing-aware representation by the weighted fusion of the experts. To alleviate the label sparsity, we further propose a batch-in dynamic discrimination enhanced (Bidden) loss weight mechanism, which can automatically assign greater loss weights to difficult samples in the training process. Both offline experiments and online A/B tests have validated the superiority of our proposed Bidden-MarfNet.