{
  "annotation_id": "546bc547-6f92-463a-b728-cc54d45c4bcc",
  "date_created": "2026-02-17T05:53:24.341000Z",
  "date_modified": "2026-02-17T05:53:24.341000Z",
  "file_hash": "18eac7009d31cdf386a26c5d166b55a5eeb1e64896b0cf872592fa73992cbbaa",
  "private": false,
  "record": {
    "abstract": "Customer Lifetime Value (LTV) prediction, a central problem in modern marketing, is characterized by a unique zero-inflated and long-tail data distribution. This distribution presents two fundamental challenges: (1) the vast majority of low-to-medium value users numerically overwhelm the small but critically important segment of high-value \"whale\" users, and (2) significant value heterogeneity exists even within the low-to-medium value user base. Common approaches either rely on rigid statistical assumptions or attempt to decouple ranking and regression using ordered buckets; however, they often enforce ordinality through loss-based constraints rather than inherent architectural design, failing to balance global accuracy with high-value precision. To address this gap, we propose \\textbf{C}onditional \\textbf{C}ascaded \\textbf{O}rdinal-\\textbf{R}esidual Networks \\textbf{(CC-OR-Net)}, a novel unified framework that achieves a more robust decoupling through \\textbf{structural decomposition}, where ranking is architecturally guaranteed. CC-OR-Net integrates three specialized components: a \\textit{structural ordinal decomposition module} for robust ranking, an \\textit{intra-bucket residual module} for fine-grained regression, and a \\textit{targeted high-value augmentation module} for precision on top-tier users. Evaluated on real-world datasets with over 300M users, CC-OR-Net achieves a superior trade-off across all key business metrics, outperforming state-of-the-art methods in creating a holistic and commercially valuable LTV prediction solution.",
    "arxiv_id": "2601.10176",
    "authors": [
      "Mingyu Zhao",
      "Haoran Bai",
      "Yu Tian",
      "Bing Zhu",
      "Hengliang Luo"
    ],
    "categories": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "CC-OR-Net: A Unified Framework for LTV Prediction through Structural Decoupling",
    "url": "https://arxiv.org/abs/2601.10176",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "4d78f3be-d163-4d96-b42c-6d7c404e3b89",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}