4 papers
From Extraction to Navigation: Progressive Retrieval with Indirectly Infinite Depth
Linxiao Che, Shanshan Huang, Haitao Lu +6
Modern large-scale recommender retrieval is shifting from static similarity matching to dynamic item space navigation, framing retrieval as iterative goal-driven graph traversal. C…
POEM: Partial-Order Enhanced Real-Time Sequential Modeling for Recommendation
Linxiao Che, Yijia Sun, Siyuan Lou +5
Real-time recommendation systems suffer from the dynamic drift of user interests and varying contextual conditions. Conventional sequential recommendation models only exploit stati…
GRank: Towards Target-Aware and Streamlined Industrial Retrieval with a Generate-Rank Framework
Yijia Sun, Shanshan Huang, Zhiyuan Guan +4
Industrial-scale recommender systems rely on a cascade pipeline in which the retrieval stage must return a high-recall candidate set from billions of items under tight latency. Exi…
MPFormer: Adaptive Framework for Industrial Multi-Task Personalized Sequential Retriever
Yijia Sun, Shanshan Huang, Linxiao Che +4
Modern industrial recommendation systems encounter a core challenge of multi-stage optimization misalignment: a significant semantic gap exists between the multi-objective optimiza…