most citedFrom Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space

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cs.IR2026

Hierarchical Quantization with Domain-Adaptive Sparse Routing for Generative Cross-Domain Recommendation

Haiying He, Xiaopeng Li, Yuchen Gu +9

Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user beh…

cs.IR2026

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…

cs.IR2026

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…

cs.IR2026

UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation

Bo Chen, Jinlong Jiao, Tijian Hu +12

Recently, substantial progress has been made in industrial recommendation through component-centric model scaling, where individual components such as behavior modeling, feature in…

cs.IR2026

OneReason Technical Report

OneRec Team, Biao Yang, Boyang Ding +81

Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. Howev…

cs.IR2026

Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling

Tianbao Ma, Ruochen Yang, Chengen Li +7

User retention is a key metric to measure long-term engagement in modern platforms. In real-time bidding (RTB) advertising system for user re-engagement, the retention model is req…