Martin Spisák

According to our database, Martin Spisák authored at least 8 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2026

From Knots to Knobs: Towards Steerable Collaborative Filtering Using Sparse Autoencoders.

Martin Spisák, Ladislav Peska, Petr Skoda, Vojtech Vancura, Rodrigo Alves

CoRR, January, 2026

Efficient Learning of Sparse Representations from Interactions.

Vojtech Vancura, Martin Spisák, Rodrigo Alves, Ladislav Peska

Proceedings of the ACM Web Conference 2026, 2026

2025

Recurrent Autoregressive Linear Model for Next-Basket Recommendation.

Tereza Zmeskalová, Antoine Ledent, Martin Spisák, Pavel Kordík, Rodrigo Alves

Proceedings of the Nineteenth ACM Conference on Recommender Systems, 2025

SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations.

Vit Kostejn, Ladislav Peska, Martin Spisák

Proceedings of the Nineteenth ACM Conference on Recommender Systems, 2025

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

Petr Kasalický, Martin Spisák, Vojtech Vancura, Daniel Bohunek, Rodrigo Alves, Pavel Kordík

Proceedings of the Nineteenth ACM Conference on Recommender Systems, 2025

Segment-Aware Analytics for Real-Time Editorial Support in Media Groups.

Martin Spisák, Rodrigo Alves, Thomas Kelleher, Jason Sheppard, Ondrej Fiedler, Ergys Kosovrasti, Vojtech Vancura, Petr Kasalický, Pavel Kordík

Proceedings of the 13th International Workshop on News Recommendation and Analytics co-located with the 2025 ACM Conference on Recommender Systems (RecSys 2025), 2025

2024

On Interpretability of Linear Autoencoders.

Martin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

2023

Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering.

Martin Spisák, Radek Bartyzal, Antonín Hoskovec, Ladislav Peska, Miroslav Tuma

Proceedings of the 17th ACM Conference on Recommender Systems, 2023