Federico Siciliano

Modern sequential recommendation systems often rely on negative sampling to efficiently train models over vast item corpora. However, common strategies such as uniform, popularity-based sampling, or hard-negative mining, often yield uninformative negatives, introduce popularity bias, or suffer fr...

We introduce a load-aware selective embedding-pruning method to be used in dense retrieval systems. Our method adapts the embedding dimensionality at inference time based on per-query deadlines and the current queue occupancy. Under light load, full-dimensional embeddings maximise effectiveness;...