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$\%$.

翻译：广告平台已演进至通过估计生命周期价值（LTV）来更好地契合广告主的真实绩效指标。然而，现实世界中LTV数据的稀疏性对LTV预测模型（即pLTV）构成了重大挑战，严重限制了其能力。因此，我们提出利用广告平台内部数据之外的外部数据，以扩大购买样本规模并增强广告平台的LTV预测模型。为解决内部与外部平台间的数据分布偏移问题，我们引入了自适应差分孪生网络（ADSNet），该网络采用跨域迁移学习以防止负迁移。具体而言，ADSNet旨在学习对目标域有益的信息。我们引入了一种增益评估策略来计算信息增益，帮助模型学习对目标域有益的信息，并提供拒绝噪声样本的能力，从而避免负迁移。此外，我们还设计了一个域适应模块作为连接不同域的桥梁，以减小域间分布距离，并增强表示空间分布的一致性。我们在真实广告平台上进行了广泛的离线实验和在线A/B测试。我们提出的ADSNet方法优于其他方法，将GINI系数提升了2$\%$。消融研究突显了增益评估策略在拒绝负增益样本和提升模型性能方面的重要性。此外，ADSNet显著改善了长尾预测效果。在线A/B测试证实了ADSNet的有效性，使在线LTV提升了3.47$\%$，商品交易总额（GMV）提升了3.89$\%$。