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cs.IR2026
Distillation-based Scenario-Adaptive Mixture-of-Experts for the Matching Stage of Multi-scenario Recommendation
Ruibing Wang, Shuhan Guo, Haotong Du +1
Multi-scenario recommendation is pivotal for optimizing user experience across diverse contexts. While Multi-gate Mixture-of-Experts (MMOE) thrives in ranking, its transfer to the…
cs.IR2025★ 3 cited
PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
Haotong Du, Yaqing Wang, Fei Xiong +5
With the expansion of business scales and scopes on online platforms, multi-scenario matching has become a mainstream solution to reduce maintenance costs and alleviate data sparsi…