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.

翻译：准确的客户终身价值预测有助于服务提供商在以客户为中心的应用中优化营销策略。然而，消费事件的严重稀疏性以及数据方差和噪声的干扰阻碍了LTV的估计。许多现有LTV预测方法直接在消费样本上训练单视角LTV预测器，这可能导致不准确甚至带有偏见的知识提取。本文提出了一种用于LTV预测的对比多视角框架，该框架是一种即插即用的解决方案，兼容各种骨干模型。它通过合成多个具有互补知识的异质LTV回归器来提升模型鲁棒性，并通过对比学习捕捉样本相关性，从而减轻对数据丰富度的依赖。具体而言，我们采用一种分解方案，将LTV预测问题转化为估计消费概率和支付金额的组合。为了减轻噪声数据对模型学习的影响，我们提出了一种多视角框架，该框架联合优化多种具有不同特点和优势的回归器，以编码和融合全面知识。为了充分利用有限训练样本的潜力，我们提出了一种混合对比学习方法，帮助在分类和回归任务中捕捉样本之间的相关性。我们在真实游戏LTV预测数据集上进行了广泛实验，结果验证了我们方法的有效性。我们已在华为移动游戏中心部署了该解决方案，并实现了总支付金额32.26%的提升。