Publication entry:
CALISSTA: Calibrated Candidate Retrieval Using Sparse Autoencoders and Top-k Aggregation
- Full citation
- Dokoupil, P., Boratto, L., & Peska, L. (2026). CALISSTA: Calibrated candidate retrieval using sparse autoencoders and top-k aggregation. Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, 345–350.
- BibTeX citation file download
- dokoupil2026calissta.bib
Internal authors
Research topics
Multimedia Retrieval
Multimedia data penetrate all areas of our lives and become more important than ever. We meet them in social media applications, video streaming services, digital libraries as well as in specialized medical or industrial fields. As multimedia data are produced using sensors, their primary representation is semantically unstructured (e.g., an image is a bunch of pixels). Hence, recognition of what actually is inside a particular multimedia document and subsequent retrieval is a hard task that requires advanced techniques for feature extraction, object detection, similarity modeling, etc. Many of these techniques are based on machine-learning models. We carry out research in various multimedia retrieval problems and also propose many topics for student academic works (Bc, Mgr, PhD).
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.