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Human-computer interaction model for multi-objective recommender systems
Machala, Patrik ; Peška, Ladislav (advisor) ; Lokoč, Jakub (referee)
One of the most developing research fields of information retrieval are recommender systems. They typically try to recommend a few of the most relevant or most suitable items to users from all the candidates when the number of canidates can be in the or- der of thousands or millions. However, it turns out that relevance alone is not enough. Therefore, this work focuses on multi-objective recommender systems using the beyond- relevance objectives. The aim of the thesis is to find out new knowledge about this specific type of recommendation, especially in the connection with the field of HCI, i.e. user and computer interaction that has not been explored much so far. The software output of the work is a web application and a modified recommender system. These two components were used in a user study, where, among other things, we investigated whether users were willing to explicitly set the parameters for a multi- objective recommender system by assigning weights to each of the objectives, compared different variants of metrics for these objectives, mechanisms for assigning weights and different level of detail of texts and visualization of the explanations of the recommen- dations. The results of our experiment show that users perceive the benefit of setting weights for objectives to improve recommendations....
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Neuroevolution-based AI for the Dominion game
Machala, Patrik ; Kuboň, David (advisor) ; Holan, Tomáš (referee)
The subject of this thesis is a simple user interface for the base version of the card game Dominon and the development of an artificial intelligence capable of playing this game. The AI is designed regardless of the initial configuration of the game. That allows an immediate start without waiting for the evolution of the opponent. The basis of this scheme is a recurrent neural network evolved by neuroevolution of its weights. Its inputs are made from representation of current game state and its output is the valuation of cards, which leads to their purchase. Second part of a player's move, the so-called action phase, is controlled by heuristics. The thesis is not limited to a single method of AI development but compares different types of evolution and different numbers of neurons in the hidden layer. According to the completed experiments the neural network evolved by competitive co-evolution with populations swapped according to their skill was the strongest opponent. Results of AI development are then compared with conclusions made on the basis of other artificial intelligencies for the Dominion game with most of these confirmed.
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