Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of consumption events and the interference of data variance and noise obstruct LTV estimation. Many existing LTV prediction methods directly train a single-view LTV predictor on consumption samples, which may yield inaccurate and even biased knowledge extraction. In this paper, we propose a contrastive multi-view framework for LTV prediction, which is a plug-and-play solution compatible with various backbone models. It synthesizes multiple heterogeneous LTV regressors with complementary knowledge to improve model robustness and captures sample relatedness via contrastive learning to mitigate the dependency on data abundance. Concretely, we use a decomposed scheme that converts the LTV prediction problem into a combination of estimating consumption probability and payment amount. To alleviate the impact of noisy data on model learning, we propose a multi-view framework that jointly optimizes multiple types of regressors with diverse characteristics and advantages to encode and fuse comprehensive knowledge. To fully exploit the potential of limited training samples, we propose a hybrid contrastive learning method to help capture the relatedness between samples in both classification and regression tasks. We conduct extensive experiments on a real-world game LTV prediction dataset and the results validate the effectiveness of our method. We have deployed our solution online in Huawei's mobile game center and achieved 32.26% of total payment amount gains.

本文提出了一种对LTV进行多视图预测的对比学习框架，通过综合多种异构的属性回归器来优化模型鲁棒性，并通过对比学习捕获样本关系来减轻对数据的依赖性，实验证明该方法可有效提高LTV预测的精度和收益。

客户终身价值预测的对比多视图框架