2 papers
cs.IR2026
Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
Zhimin Chen, Chenyu Zhao, Ka Chun Mo +7
Modern large-scale recommendation systems rely heavily on user interaction history sequences to enhance the model performance. The advent of large language models and sequential mo…
cs.IR2026
LIME: Link-based user-item Interaction Modeling with decoupled xor attention for Efficient test time scaling
Yunjiang Jiang, Ayush Agarwal, Yang Liu +1
Scaling large recommendation systems requires advancing three major frontiers: processing longer user histories, expanding candidate sets, and increasing model capacity. While prom…