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cs.IR2026
Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective
Yuening Li, Zeyu Wang, Zhen Zhang +8
Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions, faces critical stability chal…
cs.IR2026
Beyond Item IDs: Scaling Short-Form-Video Recommendation via Semantic-Native Long Sequence Modeling
Ruixiao Sun, Diego Uribe Mora, Zhimeng Jiang +7
Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by two bottlenecks: the semantic s…
cs.IR2025
Serendipitous Recommendation with Multimodal LLM
Haoting Wang, Jianling Wang, Hao Li +9
Conventional recommendation systems succeed in identifying relevant content but often fail to provide users with surprising or novel items. Multimodal Large Language Models (MLLMs)…