MDAN: Multi-distribution Adaptive Networks for LTV Prediction

Wenshuang Liu, Guoqiang Xu, Bada Ye, Xinji Luo, Yancheng He, Cunxiang Yin

Abstract: In industry, Customer Lifetime Value (LTV) represents the entire revenue generated from a single user within an application. Accurate LTV prediction can help marketers make more informed decisions about acquiring high-quality new users and increasing revenue. However, LTV prediction is a complex and challenging task, and the LTV of most application users is prone to bias and sparsity. To address these issues, this paper proposes a Multi-Distribution Adaptive Networks (MDAN) to predict LTV. In terms of classification debiasing, we leverage multi-channel networks to simultaneously learn disparate distributions and a Channel Learning Controller (CLC) is used to advance the learning of different channels. Moreover, in the context of regression debiasing, a novel loss function called Distance Similarity Loss is introduced for the specific purpose of predicting LTV. This loss function is designed to distinguish between the feature representations associated with different LTV values, thus improving the ability to represent user characteristics within the model. The MDAN framework has been successfully deployed in multiple applications within Tencent, leading to considerable increases in revenue. Extensive experiments on three million-level datasets, QB, YYB, and WeSing, demonstrate the superiority of the proposed method compared to state-of-the-art baselines such as DNN, RankSim, ZILN and ODMN models.