GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm

Shaopeng Chen; Chuyue Xie; Huimin Ren; Shaozong Zhang; Han Zhang; Ruobing Cheng; Zhiqiang Cao; Zehao Ju; Yu Gao; Jie Ding; Xiaodong Chen; Xuewu Jiao; Shuanglong Li; Liu Lin

arXiv · 2602.01865

GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm

Shaopeng Chen·Chuyue Xie·Huimin Ren·Shaozong Zhang·Han Zhang·Ruobing Cheng·Zhiqiang Cao·Zehao Ju·Yu Gao·Jie Ding·Xiaodong Chen·Xuewu Jiao·Shuanglong Li·Liu Lin

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Abstract

Traditional Deep Learning Recommendation Models (DLRMs) face increasing bottlenecks in performance and efficiency, often struggling with generalization and long-sequence modeling. Inspired by the scaling success of Large Language Models (LLMs), we propose Generative Ranking for Ads at Baidu (GRAB), an end-to-end generative framework for Click-Through Rate (CTR) prediction. GRAB integrates a novel Causal Action-aware Multi-channel Attention (CamA) mechanism to effectively capture temporal dynamics and specific action signals within user behavior sequences. Full-scale online deployment demonstrates that GRAB significantly outperforms established DLRMs, delivering a 3.05% increase in revenue and a 3.49% rise in CTR. Furthermore, the model demonstrates desirable scaling behavior: its expressive power shows a monotonic and approximately linear improvement as longer interaction sequences are utilized.

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Shaopeng Chen, Chuyue Xie, Huimin Ren, Shaozong Zhang, Han Zhang, Ruobing Cheng, Zhiqiang Cao, Zehao Ju, Yu Gao, Jie Ding, Xiaodong Chen, Xuewu Jiao, Shuanglong Li, Liu Lin. 2026-02-02. GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm. https://arxiv.org/abs/2602.01865

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