ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising

TL;DR

This paper introduces ADSNet, a cross-domain transfer learning model with an adaptive Siamese network to improve LTV prediction in advertising, especially for sparse and long-tail data, validated by offline and online experiments.

Contribution

The paper proposes ADSNet, a novel adaptive Siamese network with gain evaluation and domain adaptation modules to effectively transfer knowledge across domains for better LTV prediction.

Findings

Improves GINI by 2% over baseline methods.

Increases online LTV by 3.47% and GMV by 3.89%.

Enhances long-tail prediction accuracy.

Abstract

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$\%$.