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Scalable machine learning using Hadoop and Mahout tools
Kryške, Lukáš ; Atassi, Hicham (referee) ; Burget, Radim (advisor)
This bachelor’s thesis compares several tools for building a scalable, machine learning platform and describes their advantages and disadvantages. It also practically demonstrates functionality of this scalable platform based on the Apache Hadoop and Apache Mahout tools and measures performance of the K-Means algorithm for total of five computing nodes.
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Scalable machine learning using Hadoop and Mahout tools
Kryške, Lukáš ; Atassi, Hicham (referee) ; Burget, Radim (advisor)
This bachelor’s thesis compares several tools for building a scalable, machine learning platform and describes their advantages and disadvantages. It also practically demonstrates functionality of this scalable platform based on the Apache Hadoop and Apache Mahout tools and measures performance of the K-Means algorithm for total of five computing nodes.
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Recommendation system module analysis and design
KORTUS, Lukáš
Recommendation systems serve to users of e-commerce applications for individual recommendations to certain products or services based on their preferences. The aim of this thesis is to create a module of recommender system. The work includes analysis of recommendation systems and the methods used in these systems, including a description of the calculations. This work also solves the cold start problem, which is a problem when generation of some good recommendations for the new user is needed, but the recommendation system has no or little information about this user. Based on analysis is in this thesis designed module for recommender system, which is applicable e.g. internet for e-commerce or other internet-based application. Part of this module is the realization of a platform Apache Mahout, which some parts are built on a distributed computing platform Apache Hadoop project. Furthermore, in this work, on the aforementioned platform Mahout, selected methods of calculating the similarity using selected criteria (e.g. the average time for a recommendation, and the number of users for who have not been able to generate recommendations) are tested.
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