Generative Long-term User Interest Modeling for Click-Through Rate Prediction

• URL: http://arxiv.org/abs/2605.15905v1
• Date: Fri, 15 May 2026 12:42:54 GMT
• Title: Generative Long-term User Interest Modeling for Click-Through Rate Prediction
• Abstract summary: textbfGenerative textbfLong-term user textbfInterest model named GenLI for CTR prediction.<n>GenLI consists of an interest generation module (IGM), a behavior retrieval module (BRM), and an interest fusion module (IFM)
• Score: 11.861048898973513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 not only becomes time-consuming for online services as user behaviors continue to grow, but also overlooks the interaction information among user behaviors. To combat these problems, we propose a \textbf{Gen}erative \textbf{L}ong-term user \textbf{I}nterest model named GenLI for CTR prediction. GenLI consists of an interest generation module (IGM), a behavior retrieval module (BRM), and an interest fusion module (IFM). The IGM generates multiple interest distributions to indicate different aspects of real-time user interests, which is target-independent and incorporates interaction information among behaviors, ensuring complete and diverse interest features. The BRM selects related behaviors via a simple lookup operation, reducing the time complexity for weighting each behavior to $O(1)$. Finally, the IFM uses delicate gating mechanisms to generate interest features. Based on the generation process, GenLI improves the diversity of user interests and avoids complex matching-based behavioral retrieval, achieving a better balance between accuracy and efficiency for CTR prediction.

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