Continuous value prediction plays a crucial role in industrial-scale recommendation systems, including tasks such as predicting users' watch-time and estimating the gross merchandise value (GMV) in e-commerce transactions. However, it remains challenging due to the highly complex and long-tailed nature of the data distributions. Existing generative approaches rely on rigid parametric distribution assumptions, which fundamentally limits their performance when such assumptions misalign with real-world data. Overly simplified forms cannot adequately model real-world complexities, while more intricate assumptions often suffer from poor scalability and generalization. To address these challenges, we propose a residual quantization (RQ)-based sequence learning framework that represents target continuous values as a sum of ordered quantization codes, predicted recursively from coarse to fine granularity with diminishing quantization errors. We introduce a representation learning objective that aligns RQ code embedding space with the ordinal structure of target values, allowing the model to capture continuous representations for quantization codes and further improving prediction accuracy. We perform extensive evaluations on public benchmarks for lifetime value (LTV) and watch-time prediction, alongside a large-scale online experiment for GMV prediction on an industrial short-video recommendation platform. The results consistently show that our approach outperforms state-of-the-art methods, while demonstrating strong generalization across diverse continuous value prediction tasks in recommendation systems.

翻译：连续值预测在工业级推荐系统中发挥着关键作用，包括预测用户观看时长、估算电子商务交易中的商品交易总额（GMV）等任务。然而，由于数据分布具有高度复杂性和长尾特性，该任务仍面临挑战。现有的生成式方法依赖于僵化的参数分布假设，当这些假设与现实数据不匹配时，其性能会受到根本性限制。过于简化的形式无法充分建模现实世界的复杂性，而更复杂的假设往往存在可扩展性和泛化性不足的问题。为解决这些挑战，我们提出一种基于残差量化（RQ）的序列学习框架，该框架将目标连续值表示为有序量化码的累加和，并通过从粗粒度到细粒度的递归预测实现逐渐减小的量化误差。我们引入了一种表征学习目标，使RQ码嵌入空间与目标值的序数结构对齐，从而让模型能够学习量化码的连续表征，进一步提升预测精度。我们在生命周期价值（LTV）和观看时长预测的公共基准测试上进行了广泛评估，同时在工业级短视频推荐平台上开展了大规模在线GMV预测实验。结果一致表明，我们的方法优于现有最优方法，并在推荐系统的多种连续值预测任务中展现出强大的泛化能力。