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arXiv Machine Learning By Runpeng Cui, Zhipeng Sun, Chi Lu, Peng Jiang

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

Read the original on arXiv Machine Learning →

The paper introduces RQ-Reg, a residual‑quantization framework for predicting continuous values in recommender systems. It decomposes target values into a sequence of quantization codes, autoregressively refining predictions from coarse to fine granularity, and incorporates an ordinal‑aware objective to align embeddings with target order. Experiments on watch‑time, LTV, and a large‑scale online A/B test for GMV demonstrate competitive performance and strong generalization across diverse prediction tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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