In computational advertising, platforms are increasingly optimizing toward advertisers' real assessment metrics to help achieve more reliable advertising performance. Consequently, predicting customers' Lifetime Value (LTV) has become an essential component of the advertising system, as it directly impacts the actual Return On Investment (ROI) of advertisers. Recent research on LTV prediction primarily focuses on the ranking stage, lacking consideration of the initial retrieval stage. This oversight may lead to the inconsistency between retrieval and ranking, resulting in a loss of efficiency. Unlike the LTV estimation in the ranking stage, the retrieval stage faces more severe data sparsity and constraints inherent in online scoring. Incorporating rich data from other domains can mitigate the sparsity while introducing the negative transfer issue. To tackle these challenges, we introduce Retrieval-LTV, a two-tower retrieval model for LTV prediction. This model employs a cooperative framework and incorporates a fine-grained evaluation for each sample across each expert, thereby enhancing effective selective learning from the source domain while mitigating the risk of negative transfer. Additionally, we have designed a specialized representation transformation to obtain the LTV-oriented score for online retrieval. Experiments on three real-world industrial datasets demonstrate that Retrieval-LTV outperforms all the baselines, achieving superior performance. An online A/B test further confirms the effectiveness of Retrieval-LTV, increasing the overall LTV by 2.08%. As a result, Retrieval-LTV has now been fully deployed in Tencent Ads.

Advertising platforms have evolved in estimating Lifetime Value (LTV) to better align with advertisers' true performance metric which considers cumulative sum of purchases a customer contributes over a period. Accurate LTV estimation is crucial for the precision of the advertising system and the effectiveness of advertisements. 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%.