Kuldeep Singh Sidhu’s Post

Scaling Laws Meet Click-Through Rate Prediction: A New Paradigm for Online Performance Researchers from the Chinese Academy of Sciences and Meituan have introduced SUAN (Stacked Unified Attention Network), a breakthrough approach that brings Large Language Model scaling principles to CTR prediction. The Core Innovation: SUAN implements a unified attention block (UAB) that combines three attention mechanisms: - Self-attention to capture spatiotemporal dependencies in user behavior sequences - Cross-attention to analyze behaviors from user profile perspectives   - Dual alignment attention to selectively emphasize informative features while suppressing noise Technical Architecture: The model incorporates LLM-inspired components like RMSNorm for training stability and SwiGLU-based feedforward networks. The key insight is treating "model grade" as a combination of model size and behavior sequence length - recognizing that high-performance CTR models should benefit from longer user sequences. Deployment Strategy: To solve the accuracy vs. latency trade-off, the team developed LightSUAN with sparse self-attention (combining local and dilated attention patterns) and parallel inference strategies. They then use online distillation to transfer knowledge from high-grade SUAN models to deployable LightSUAN variants. Real-World Impact: The distilled LightSUAN achieved remarkable results in production: 2.81% CTR improvement and 1.69% CPM increase while maintaining acceptable 43ms inference time. The model demonstrates scaling laws across three orders of magnitude in both model parameters and data size. This work bridges the gap between theoretical scaling advances and practical deployment constraints in recommendation systems, offering a new paradigm for improving online prediction performance.