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Visual Explanations in Music Recommender Systems
Savčinský, Richard ; Peška, Ladislav (advisor) ; Petříček, Tomáš (referee)
Music recommendations from industry-leading algorithms are a product of a hybrid system combining multiple techniques. However, in the end, the user is simply left without additional information why a certain song is present in the result. One way to improve the experience for the user is to provide so-called visual explanations. For that purpose, in this thesis, we designed and proposed various forms of visual explanations for the recommended data from the Spotify API. The main goal was to highlight important hidden relationships between familiar and new music, used by Spotify but also to utilize the actual audio features for the construction of our own recommender system. We developed a modern mobile application that allows users to explore and interact with the visualizations of their own music tastes and also provides tools to customize the experience.
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Echo state networks and their application in time series prediction
Savčinský, Richard ; Mráz, František (advisor) ; Matzner, Filip (referee)
Recurrent neural networks (RNN) enable to model dynamical sys- tems with variable input length. Their disadvantage is in inherently difficult trai- ning which means adjusting weights of connections between neurons connected in the network. Echo state networks (ESN) are a special type of RNN which are by contrast trainable rather simply. They include a reservoir of neurons whose state reflect the history of all signals in the network and that is why this type of network is suitable for simulation and prediction of time series. To maximize the computational power of ESN, very precise adjusting and experimenting are required. Because of that, we have created a tool for building and testing such networks. We have implemented a time series forecasting task for the purpose of examination of our tool. We have focused on stock price prediction, which repre- sents an uncertain and complicated area for achieving precise results in. We have compared our tool to other tools and it was comparably successful. 1
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Echo state networks and their application in time series prediction
Savčinský, Richard ; Mráz, František (advisor) ; Matzner, Filip (referee)
Recurrent neural networks (RNN) enable to model dynamical sys- tems with variable input length. Their disadvantage is in inherently difficult trai- ning which means adjusting weights of connections between neurons connected in the network. Echo state networks (ESN) are a special type of RNN which are by contrast trainable rather simply. They include a reservoir of neurons whose state reflect the history of all signals in the network and that is why this type of network is suitable for simulation and prediction of time series. To maximize the computational power of ESN, very precise adjusting and experimenting are required. Because of that, we have created a tool for building and testing such networks. We have implemented a time series forecasting task for the purpose of examination of our tool. We have focused on stock price prediction, which repre- sents an uncertain and complicated area for achieving precise results in. We have compared our tool to other tools and it was comparably successful.
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