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PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
Haotong Du, Yaqing Wang, Fei Xiong, Lei Shao, Ming Liu, Hao Gu, Quanming Yao, Zhen Wang
https://arxiv.org/abs/2506.18382

PERSCEN: Learning Personalized Interaction Pattern and Scenario Preference for Multi-Scenario Matching
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 sparsity. The key to effective multi-scenario recommendation lies in capturing both user preferences shared across all scenarios and scenario-aware preferences specific to each scenario. However, existing methods often overlook user-specific modeling, limiting the generation of personalized user representations. To address …