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ARXIV:2605.15905 · CTR PREDICTION · SUBMITTED 18 MAY · 20:33 UTC · FRESHNESS STALE
ARXIV:2605.15905CTR PREDICTIONSUBMITTED 18 MAY · 20:33 UTCFRESHNESS STALEJiangli Shao · Kaifu Zheng · Hao Fang · Huimu Ye · Zhiwei Liu · Bo Zhang · +2 at arXiv
A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency.
Opportunity summary
Pain A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency. Typically, a two-stage framework is widely adopted, where a general search unit (GSU) first retrieves top-$k$ relevant behaviors towards…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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…
CTR Prediction moved forward this cycle; last verified May 2026. Public score 3.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency.
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10.48550/arXiv.2605.15905A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency.
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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Dimensions overall score 3.0
PROBLEM
A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency. 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...
METHOD
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-...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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 p...
WHY NOW
CTR Prediction moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
CTR Prediction moved forward this cycle; last verified May 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A generative model for long-term user interest prediction to improve click-through rate accuracy and efficiency.
Segment
CTR Prediction
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3.0/10 public viability
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