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Recommender systems for culture events
 Vytisková, Zuzana ; Peška, Ladislav (advisor) ; Kopecký, Michal (referee)
The diploma thesis deals with the topic of recommendation in culture. In the theoretical part, it compares the recommendation of digitally available works with event recommendations, which serves as the basis for describing recommendations on the cultural portal. Further, the thesis examines the domain model as several different interconnected types of objects. Using these relations to enrich data sets allows overcoming the low data density and improving the recommendations. The paper examines two common situations of practical recommendation, general user recommendation with minimal profile and recommendation to registered users with known history. As a part of the solution, hybrid algorithms have been implemented based on the introducing content information into existing collaborative filtering methods. The results are verified in offline tests on data sets consisting of both research and real-world data. The subjective quality of the resulting recommendations was examined through a user study.
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