| Abstract: |  In the recent decades and especially in the last few years, we have experienced a rapid growth of information. In the following years, the amount of data is supposed to multiply by hundreds. With growing amount of data, the need for high-quality Information Retrieval and for correct ranking of the retrieved results is rapidly increasing. Learning to Rank, as supervised machine learning methods, can help solving the issue which is present in many applications, such as web search engines, recommendation systems or misspelling corrections. This thesis provides an exhaustive listing and analysis of current state-of-the-art algorithms and it describes the necessary background for this work. Besides, it focuses on applicable performance measures and available datasets. All the hypothesis and knowledge are utilized in a thorough set of experiments. As LambdaMART was evaluated as the potentially best LTR algorithm, our own implementation of the algorithm is introduced and compared to an existing implementation. On the one hand, this thesis can server as a guide to any researcher interested in this topic and on the other it opens many new questions and issues.
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