3 papers
cs.IR2026
Efficient Learning of Sparse Representations from Interactions
Vojtěch Vančura, Martin Spišák, Rodrigo Alves +1
Behavioral patterns captured in embeddings learned from interaction data are pivotal across various stages of production recommender systems. However, in the initial retrieval stag…
cs.IR2026
From Knots to Knobs: Towards Steerable Collaborative Filtering Using Sparse Autoencoders
Martin Spišák, Ladislav Peška, Petr Škoda +2
Sparse autoencoders (SAEs) have recently emerged as pivotal tools for introspection into large language models. SAEs can uncover high-quality, interpretable features at different l…
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…