# RQ-Reg: A Residual-Quantization-Based Framework for Continuous Value Prediction in Recommender Systems

arXiv: https://arxiv.org/abs/2602.23012 · HTML: https://mlsanity.com/paper/2602.23012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Autoregressive Image Generation using Residual Quantization](https://mlsanity.com/paper/2203.01941/) (2022)
- [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022)
- [Rank-N-Contrast: Learning Continuous Representations for Regression](https://mlsanity.com/paper/2210.01189/) (2022)
- [LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders](https://mlsanity.com/paper/2505.04421/) (2025)
- [Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation](https://mlsanity.com/paper/2206.06003/) (2022)
- [QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou](https://mlsanity.com/paper/2411.11739/) (2024)
- [Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search](https://mlsanity.com/paper/1806.08211/) (2018)
- [Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation](https://mlsanity.com/paper/2306.03392/) (2023)
- [Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou](https://mlsanity.com/paper/2208.13358/) (2022)
- [Customer Lifetime Value Prediction Using Embeddings](https://mlsanity.com/paper/1703.02596/) (2017)
- [CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems](https://mlsanity.com/paper/2401.07521/) (2024)
- [DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias](https://mlsanity.com/paper/2208.05190/) (2022)
- [Generative Regression Based Watch Time Prediction for Short-Video Recommendation](https://mlsanity.com/paper/2412.20211/) (2024)
- [Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network](https://mlsanity.com/paper/2508.12665/) (2025)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Exponential-Gaussian Mixtures for Watch-Time Distribution Prediction](https://mlsanity.com/paper/2608.23356/) (2026)

## Abstract (arXiv)

Predicting continuous values such as watch-time and gross merchandise value (GMV) is a core problem in industrial recommendation systems. Its inherent difficulty stems from the highly complex and long-tailed distributions of the target signals, which are hard to model accurately. Existing regression methods typically rely on fixed parametric assumptions on the target distribution: overly simple assumptions underfit real-world data, whereas more intricate ones tend to sacrifice scalability and generalization. To address these limitations, we propose a sequence modeling framework based on residual quantization (RQ), in which the target continuous value is decomposed into a sequence of quantization codes that represent progressively finer approximations. The model autoregressively predicts these codes from coarse to fine granularity, with each step refining the residual error left by the previous one. To further improve the quality of the learned representations, we introduce an ordinal-aware representation learning objective that aligns the RQ code embedding space with the ordinal structure of target values, thereby yielding continuous representations of quantization codes and more accurate predictions. We conduct comprehensive experiments on public benchmarks for watch-time and lifetime value (LTV) prediction, together with a large-scale online A/B test for GMV prediction on an industrial short-video recommendation platform. Across all settings, the proposed method shows competitive performance among existing state-of-the-art approaches and generalizes well across diverse continuous value prediction scenarios.