Publication entry:
Product exploration based on latent visual attributes
- Full citation
- Skopal, T., Peška, L., Kovalčík, G., Grosup, T., & Lokoč, J. (2017). Product exploration based on latent visual attributes. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2531–2534.
- BibTeX citation file download
- skopal2017product.bib
Internal authors
Tomáš Skopal
Deputy head of department, Professor
Ladislav Peška
Associate professor
Jakub Lokoč
Associate professor
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.
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).