The main purpose of this master thesis was to develop and evaluate innovative approach for selection of optimal related web article. We often do not have access to data about user's profiles and their preferences. Therefore, these settings have an important influence to our research. Consequently, we decided to use Multi-Armed bandit approach to deal with described settings. Comparison of different MAB algorithms has been done on Jackpot problem, which was designed for Celtra programming challenge. The main research problem has been simulated and evaluated on a real data obtained from a provider of related web content. We tried to improve existing recommendation system with reordering similar news to currently read one. Using this approach, the most interesting news have been recommended on top positions and the most promising news have been explored as well. In this master thesis, we tried to answer to the following questions: does it make sense to take into account statistics of recommended news in context of every news stream separately; is it possible to approximate click-through rate using content-related recommendation streams; could we achieve better results, if we approximate initial input parameters of beta distribution using Bayesian approach. Our most promising method has achieved more than 40% average position improvement regarding random selection strategy of content-related recommendations.
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