mlsanityData to 7 Oct 2026

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

arXiv 2602.01865 ↗First posted 2 Feb 2026cs.IR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 54 of its 62 references that are in the map. First posted on arXiv 2 Feb 2026.

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References · 54

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