Advertising platforms have evolved in estimating Lifetime Value (LTV) to
better align with advertisers' true performance metric. However, the sparsity
of real-world LTV data presents a significant challenge to LTV predictive
model(i.e., pLTV), severely limiting the their capabilities. Therefore, we
propose to utilize external data, in addition to the internal data of
advertising platform, to expand the size of purchase samples and enhance the
LTV prediction model of the advertising platform. To tackle the issue of data
distribution shift between internal and external platforms, we introduce an
Adaptive Difference Siamese Network (ADSNet), which employs cross-domain
transfer learning to prevent negative transfer. Specifically, ADSNet is
designed to learn information that is beneficial to the target domain. We
introduce a gain evaluation strategy to calculate information gain, aiding the
model in learning helpful information for the target domain and providing the
ability to reject noisy samples, thus avoiding negative transfer. Additionally,
we also design a Domain Adaptation Module as a bridge to connect different
domains, reduce the distribution distance between them, and enhance the
consistency of representation space distribution. We conduct extensive offline
experiments and online A/B tests on a real advertising platform. Our proposed
ADSNet method outperforms other methods, improving GINI by 2$\%$. The ablation
study highlights the importance of the gain evaluation strategy in negative
gain sample rejection and improving model performance. Additionally, ADSNet
significantly improves long-tail prediction. The online A/B tests confirm
ADSNet's efficacy, increasing online LTV by 3.47$\%$ and GMV by 3.89$\%$.

利用 ADSNet 方法，通过使用外部数据扩展购买样本的规模，并提升广告平台的生命周期价值（LTV）预测模型，从而解决了真实世界 LTV 数据稀缺性所带来的问题。