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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Predicting the Behaviour of Streaming Services Users
 Stachura, Šimon ; Pilát, Martin (advisor) ; Peška, Ladislav (referee)
Title: Predicting the Behaviour of Streaming Services Users Autor: Bc. Šimon Stachura Department: Katedra teoretické informatiky a matematické logiky Supervisor: Mgr. Martin Pilát, Ph. D. Abstract: Streaming services are one of the phenomena of the last decade, allowing online legal access to media for a large number of users. The media is usually provided to the users as an automatically generated sequence, created by some form of a recommender system. The preferences of individual users are usually estimated based on historical data from their previous usage of the service. Skipping behaviour on individual elements of the generated sequence (songs, for instance) is one of the basic signals expressing these preferences. Goal of this work is to predict users' behaviour based on their previous experience with the service. We chose a large dataset consisting of real data from usage of the Spotify service, and considered options for preprocessing and representing them. We decided to use recurrent neural networks with the Encoder-Decoder architecture for modelling the behaviour of the users. These models encode the information about users' historical behaviour into a compact inner representation of the session, and based on that representation they generate expected behaviour in the next time steps. We created...
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Hearthstone Counter-Deck Builder
 Stachura, Šimon ; Gemrot, Jakub (advisor) ; Pilát, Martin (referee)
1 Title: Hearthstone Counter-Deck Builder Author: Šimon Stachura Department: Katedra softwaru a výuky informatiky Supervisor: Mgr. Jakub Gemrot, Ph. D. Abstract: Collecting cards and building decks out of them is the basic principle of collectible card games (such as Hearthstone). This task is usually very complex and requires players to think about a lot of factors, such as stability of deck's results or interactions among cards. The goal of this work is to try to make deckbuilding for Hearthstone automatic. Hill-climbing algorithm was used for this task. Generated decks were evaluated based on their winrate against chosen human-built actual decks from the game. Usage of hill-climbing brought a lot of questions - for instance, how to restrict the huge space of possible decks, what artifical intelligence to use for games' simulation, or how to make the simulation stable enough in such a non- deterministic environment. We have also tried to apply a new approach to a few of these problems. We have conducted two experiments to test our approach. Both experimentally created decks reached about 80 percent winrate against human-made decks. The results proved that even in such a nondeterministic environment hill- climbing is able to find interesting solutions. However, these solutions are highly dependent on...
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