1 citations · 1 across the 1 of their papers we have counts for
3 papers
Efficient Learning of Sparse Representations from Interactions
VojtÄch VanÄura, 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…
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…
The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems
Petr Kasalický, Martin Spišák, VojtÄch VanÄura +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…