  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.
