most citedAn End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation

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cs.IR2026

RECAP: Feedback-Driven Streaming Semantic User Profiles for Short-Video Recommendation

Ziyi Zhao, Xiaoyou Zhou, Xiao Lv +13

Language-based user profiles convert long behavioral histories into explicit semantic representations for recommendation. However, most profile generators are optimized in an open…

cs.IR2026

Bending the Scaling Law Curve in Large-Scale Recommendation Systems

Qin Ding, Kevin Course, Linjian Ma +19

Learning from user interaction history through sequential models has become a cornerstone of large-scale recommender systems. Recent advances in large language models have revealed…

cs.IR2026

SARM: LLM-Augmented Semantic Anchor for End-to-End Live-Streaming Ranking

Ruochen Yang, Yueyang Liu, Zijie Zhuang +14

Large-scale live-streaming recommendation requires precise modeling of non-stationary content semantics under strict real-time serving constraints. In industrial deployment, two co…

cs.IR2026

QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling

Tian Xia, Jiaqi Zhang, Yueyang Liu +25

With the evolution of large language models (LLMs), there is growing interest in leveraging their rich semantic understanding to enhance industrial recommendation systems (RecSys).…

cs.IR2026

OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce

Kun Zhang, Jingming Zhang, Wei Cheng +29

In the wave of generative recommendation, we present OneMall, an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou. Our OneMall systematic…

cs.IR2026

Towards End-to-End Alignment of User Satisfaction via Questionnaire in Video Recommendation

Na Li, Jiaqi Yu, Minzhi Xie +8

Short-video recommender systems typically optimize ranking models using dense user behavioral signals, such as clicks and watch time. However, these signals are only indirect proxi…