AI RESEARCH
Generative Long-term User Interest Modeling for Click-Through Rate Prediction
arXiv CS.AI
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ArXi:2605.15905v1 Announce Type: cross Modeling long-term user interests with massive historical user behaviors enhances click-through rate (CTR) prediction performance in advertising and recommendation systems. Typically, a two-stage framework is widely adopted, where a general search unit (GSU) first retrieves top-$k$ relevant behaviors towards the target item, and an exact search unit (ESU) generates interest features via tailored attention. However, current target-centered GSU would ignore other latent user interests, leading to incomplete and biased interest features.