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About this paper
Mingyu Zhao, Haoran Bai, Yu Tian, Bing Zhu, Hengliang Luo
Researchers from Meituan and Renmin University of China developed CC-OR-Net, a unified framework that employs structural decoupling to predict Customer Lifetime Value (LTV). This approach achieves a balanced optimization of ranking quality, regression accuracy, and high-value precision, demonstrating superior user stratification with up to 67.01% Stratified Value Accuracy and a 25% reduction in high-value prediction bias on industrial datasets.
Key insights
- Architecturally ensuring ordinality through cascaded decomposition provides a robust foundation for LTV ranking, transforming the inherent "trilemma" conflicts into manageable trade-offs.
- Directly addressing the sparsity of high-value "whale" users with an attention-guided augmentation module significantly improves precision for this critical segment.
- Synergistic integration of specialized modules, including conditional cascading, feature alignment, and intra-bucket residual learning, effectively handles complex LTV data distributions and diverse prediction objectives.
Summary by alphaXiv.
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