Martin Spišák

The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems

2025 · arXiv (Cornell University)

Industry-scale recommender systems face a core challenge: representing entities with high cardinality, such as users or items, using dense embeddings that must be accessible during both training and inference. However, as embedding sizes grow, memory …