394 citations · 400 across the 5 of their papers we have counts for
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cs.IR2026
UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
Rongcheng Lin, Yan Sun, Jamey Zhang +4
Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models ov…
cs.IR2026
Memory Layer: Train the In-Model Cache for Recommendation Models
Liangyuan Na, Gufan Yin, Yixin Bao +19
Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Because this cache exists only at s…
cs.IR2026
SlimPer: Make Personalization Model Slim and Smart
Siqi Wang, Xianjie Chen, Shaofeng Deng +42
Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely o…