1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.IR2025
The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems
Petr Kasalický, Martin Spišák, Vojtěch Vančura +3
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…
cs.IR2023★ 1 cited
Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders
Petr Kasalický, Rodrigo Alves, Pavel Kordík
The evaluation of recommendation systems is a complex task. The offline and online evaluation metrics for recommender systems are ambiguous in their true objectives. The majority o…