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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

Decoupled Multimodal Fusion for User Interest Modeling in Click-Through Rate Prediction

Alin Fan, Hanqing Li, Sihan Lu +2

Modern industrial recommendation systems improve recommendation performance by integrating multimodal representations from pre-trained models into ID-based Click-Through Rate (CTR)…

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…