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cs.IR2026
Preserving rare-item recall under a storage budget: quantization, distillation, and the limits of teacher fidelity
Han Chen, Yingrui Li
Under a fixed embedding-storage budget, recommenders can use a small full-precision model, a distilled student, or a higher-rank low-precision model. We asked which choice preserve…
cs.IR2026
Candidate-dependent residual routing trades top-of-list accuracy for recall in sequential recommendation
Han Chen, Yingrui Li
Candidate-dependent routing lets a sequential recommender weight a user's past behaviors differently for each item it scores. We test whether this flexibility helps by adding a sma…
cs.IR2025
STARS: Semantic Tokens with Augmented Representations for Recommendation at Scale
Han Chen, Steven Zhu, Yingrui Li
Real-world ecommerce recommender systems must deliver relevant items under strict tens-of-milliseconds latency constraints despite challenges such as cold-start products, rapidly s…