CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction 事件

CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction arXiv:2508.03668v2 Announce Type: replace Abstract: Click-Through Rate (CTR) prediction, a core task in recommendation systems, estimates user click likelihood using historical behavioral data. Modeling user behavior sequences as text to leverage Language Models (LMs) for this task has gained traction, owing to LMs' strong semantic understanding and contextual modeling capabilities. However, a critical structural gap