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cs.IR2026
Read More, Reason Deeper: Latent Workspaces for Long-Sequence Recommendation
Renzhi Wu, Li Sheng, Zikun Cui +8
Long-sequence recommendation can improve along two axes: \emph{evidence scaling} exposes longer histories, while \emph{reasoning scaling} adds layers that interpret that evidence f…
cs.IR2026
PRISM: Personalized Item Representations in User History
Renzhi Wu, Zikun Cui, Junjie Yang +6
Sequential recommenders typically construct history inputs from item identifiers or content features using an embedding map shared across users. The same item therefore yields the…
cs.IR2026
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Renzhi Wu, Zikun Cui, Junjie Yang +10
Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet exi…