AI RESEARCH
CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
arXiv CS.CL
•
ArXi:2508.03668v2 Announce Type: replace 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 exists: user behavior sequences consist of discrete actions connected by semantically empty separators, differing fundamentally from the coherent natural language in LM pre-