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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Fairness in group recommender systems
 Maleček, Ladislav ; Peška, Ladislav (advisor) ; Lokoč, Jakub (referee)
The goal of this thesis is to explore the area of group recommender systems with an emphasis on fairness. In the core part of our thesis, we have created a novel aggregation method called Exactly Proportional Fuzzy D'Hondt's Aggrega- tion that works on top of single-user recommender systems. We have evaluated it on five datasets, in three different recommendation scenarios, and with two different types of artificially created groups. The proposed algorithm performed favorably with respect to several fairness metrics while maintaining a reasonable utility of the recommendation. Furthermore, we have created a set of tools to simplify the evaluation pipeline of group recommender systems. The main parts of the pipeline are a dataset downloader, matrix factorizer, and synthetic group creation scripts. We believe these tools may contribute towards more reproducible research in the group recommender systems domain. 1
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Books Recommender System via Linked Open Data
 Maleček, Ladislav ; Peška, Ladislav (advisor) ; Škoda, Petr (referee)
This thesis focuses on using recommender system's methods on Linked Open Data in a domain of books. After thorough analysis of multiple available Linked Open Data sets, we have concluded that data sets of sufficient size and quality already exist. Together with careful analysis of the structure and quality of the data, recommender system web application has been developed based on retrieved data from a Wikidata endpoint. The application design allows an incorporation of data from multiple sources. A novel approach for generating recommendations utilizing multi language tags extracted from Wikipedia was used. We have shown that it is possible and viable to use recommender systems on top of the Linked Open Data, but the common recommender system's algorithms have to be modified in order to deal with a huge amount of sparsity in the data.
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3 Maleček, Lukáš
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