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20242026
most citedHeterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2026

From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling

Fan Li, Chang Meng, Jiaqi Fu +6

Modern online platforms increasingly adopt multi-page architectures to accommodate diverse user needs. On these platforms, page navigation (the process of directing users to specif…

cs.IR20251 cited

Heterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation

Chenhao Zhai, Chang Meng, Xueliang Wang +5

The rapid proliferation of short videos on social media platforms presents unique challenges and opportunities for recommendation systems. Users exhibit diverse preferences, and th…

cs.IR2025

KLAN: Kuaishou Landing-page Adaptive Navigator

Fan Li, Chang Meng, Jiaqi Fu +5

Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the…

cs.IR2025

Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation

Qing Yu, Xiaobei Wang, Shuchang Liu +14

Recommender systems filter contents/items valuable to users by inferring preferences from user features and historical behaviors. Mainstream approaches follow the learning-to-rank…

cs.IR2024

Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation

Chang Meng, Chenhao Zhai, Xueliang Wang +6

With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommend…