EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling

• URL: http://arxiv.org/abs/2602.10811v1
• Date: Wed, 11 Feb 2026 12:51:54 GMT
• Title: EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling
• Authors: Mingyang Liu, Yong Bai, Zhangming Chan, Sishuo Chen, Xiang-Rong Sheng, Han Zhu, Jian Xu, Xinyang Chen,
• Abstract summary: Efficiently scaling industrial Click-Through Rate (CTR) prediction has recently attracted significant research attention.<n>We propose the Efficiently Scalable Transformer (EST), which achieves fully unified modeling by processing all raw inputs in a single sequence without lossy aggregation.<n>EST significantly outperforms production baselines, delivering a 3.27% RPM (Revenue Per Mile) increase and a 1.22% CTR lift.
• Score: 13.693397814262681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficiently scaling industrial Click-Through Rate (CTR) prediction has recently attracted significant research attention. Existing approaches typically employ early aggregation of user behaviors to maintain efficiency. However, such non-unified or partially unified modeling creates an information bottleneck by discarding fine-grained, token-level signals essential for unlocking scaling gains. In this work, we revisit the fundamental distinctions between CTR prediction and Large Language Models (LLMs), identifying two critical properties: the asymmetry in information density between behavioral and non-behavioral features, and the modality-specific priors of content-rich signals. Accordingly, we propose the Efficiently Scalable Transformer (EST), which achieves fully unified modeling by processing all raw inputs in a single sequence without lossy aggregation. EST integrates two modules: Lightweight Cross-Attention (LCA), which prunes redundant self-interactions to focus on high-impact cross-feature dependencies, and Content Sparse Attention (CSA), which utilizes content similarity to dynamically select high-signal behaviors. Extensive experiments show that EST exhibits a stable and efficient power-law scaling relationship, enabling predictable performance gains with model scale. Deployed on Taobao's display advertising platform, EST significantly outperforms production baselines, delivering a 3.27\% RPM (Revenue Per Mile) increase and a 1.22\% CTR lift, establishing a practical pathway for scalable industrial CTR prediction models.

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