Accurately modeling long-term value (LTV) at the ranking stage of short-video recommendation remains challenging. While delayed feedback and extended engagement have been explored, fine-grained attribution and robust position normalization at billion-scale are still underdeveloped. We propose a practical ranking-stage LTV framework addressing three challenges: position bias, attribution ambiguity, and temporal limitations. (1) Position bias: We introduce a Position-aware Debias Quantile (PDQ) module that normalizes engagement via quantile-based distributions, enabling position-robust LTV estimation without architectural changes. (2) Attribution ambiguity: We propose a multi-dimensional attribution module that learns continuous attribution strengths across contextual, behavioral, and content signals, replacing static rules to capture nuanced inter-video influence. A customized hybrid loss with explicit noise filtering improves causal clarity. (3) Temporal limitations: We present a cross-temporal author modeling module that builds censoring-aware, day-level LTV targets to capture creator-driven re-engagement over longer horizons; the design is extensible to other dimensions (e.g., topics, styles). Offline studies and online A/B tests show significant improvements in LTV metrics and stable trade-offs with short-term objectives. Implemented as task augmentation within an existing ranking model, the framework supports efficient training and serving, and has been deployed at billion-scale in Taobao's production system, delivering sustained engagement gains while remaining compatible with industrial constraints.

翻译：在短视频推荐的排序阶段准确建模长期价值（LTV）仍然具有挑战性。尽管延迟反馈和扩展参与度已被探索，但在十亿级规模下的细粒度归因和鲁棒的位置归一化仍然发展不足。我们提出了一个实用的排序阶段LTV框架，旨在解决三个挑战：位置偏差、归因模糊性和时间局限性。（1）位置偏差：我们引入了位置感知去偏分位数（PDQ）模块，通过基于分位数的分布对参与度进行归一化，实现在无需架构更改的情况下进行位置鲁棒的LTV估计。（2）归因模糊性：我们提出了一个多维度归因模块，该模块学习跨上下文、行为和内容信号的连续归因强度，取代静态规则以捕捉细微的视频间影响。一个带有显式噪声过滤的定制化混合损失提高了因果清晰度。（3）时间局限性：我们提出了一个跨时间作者建模模块，该模块构建了考虑删失的、天级别的LTV目标，以捕捉创作者驱动的长期再参与；该设计可扩展至其他维度（例如主题、风格）。离线研究和在线A/B测试显示，LTV指标显著提升，并与短期目标保持稳定的权衡。该框架作为现有排序模型中的任务增强实现，支持高效的训练和服务，并已在淘宝生产系统中以十亿级规模部署，在保持与工业约束兼容的同时，带来了持续的参与度增益。