Publication entry:

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

Full citation: Koštejn, V., Peška, L., & Spišák, M. (2025). SAGEA: Sparse autoencoder-based group embeddings aggregation for fairness-preserving group recommendations. Proceedings of the Nineteenth ACM Conference on Recommender Systems, 1290–1295.
BibTeX citation file download: kostejn2025sagea.bib

Internal authors

Ladislav Peška

Associate professor

Martin Spišák

Assistant researcher

Research topics

Recommender systems

Software that estimates, models and evaluates user preferences and needs, producing viable recommendations of items such as multimedia or goods that would match user interests.