RQ-GMM: Residual Quantized Gaussian Mixture Model for Multimodal Semantic Discretization in CTR Prediction

• URL: http://arxiv.org/abs/2602.12593v1
• Date: Fri, 13 Feb 2026 04:11:24 GMT
• Title: RQ-GMM: Residual Quantized Gaussian Mixture Model for Multimodal Semantic Discretization in CTR Prediction
• Authors: Ziye Tong, Jiahao Liu, Weimin Zhang, Hongji Ruan, Derick Tang, Zhanpeng Zeng, Qinsong Zeng, Peng Zhang, Tun Lu, Ning Gu,
• Abstract summary: Discretizing embeddings into semantic IDs before feeding them into CTR models offers a more effective solution.<n>We propose RQ-GMM, which introduces probabilistic modeling to better capture the statistical structure of multimodal embedding spaces.<n>RQ-GMM achieves superior codebook utilization and reconstruction accuracy.
• Score: 29.97246591569267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal content is crucial for click-through rate (CTR) prediction. However, directly incorporating continuous embeddings from pre-trained models into CTR models yields suboptimal results due to misaligned optimization objectives and convergence speed inconsistency during joint training. Discretizing embeddings into semantic IDs before feeding them into CTR models offers a more effective solution, yet existing methods suffer from limited codebook utilization, reconstruction accuracy, and semantic discriminability. We propose RQ-GMM (Residual Quantized Gaussian Mixture Model), which introduces probabilistic modeling to better capture the statistical structure of multimodal embedding spaces. Through Gaussian Mixture Models combined with residual quantization, RQ-GMM achieves superior codebook utilization and reconstruction accuracy. Experiments on public datasets and online A/B tests on a large-scale short-video platform serving hundreds of millions of users demonstrate substantial improvements: RQ-GMM yields a 1.502% gain in Advertiser Value over strong baselines. The method has been fully deployed, serving daily recommendations for hundreds of millions of users.

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