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洞見 - Click-through rate prediction
Retrieval-Augmented Transformer for Click-Through Rate Prediction
This paper proposes a Retrieval-Augmented Transformer (RAT) model that enhances fine-grained intra- and cross-sample feature interactions to improve click-through rate prediction.
Enhancing Efficiency and Effectiveness of LLM-Based Click-Through Rate Prediction with Long Textual User Behaviors
BAHE, a novel hierarchical encoding approach, decouples the representation extraction of atomic behaviors from the learning of behavior interactions, significantly improving the efficiency and effectiveness of LLM-based CTR prediction with long user sequences.
