Generative Long-term User Interest Modeling for Click-Through Rate Prediction
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. Additionally, the matching-based retrieval process in GSUs depends on the pairwise similarity score between target item and each historical behavior, which ...
| Authors | Jiangli Shao, Kaifu Zheng, Hao Fang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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