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Preferencev querying, indexing, optimisation
Horničák, Erik ; Vojtáš, Peter (advisor) ; Ondreička, Matúš (referee)
In this thesis we discuss the issue of searching the best k objects from the multi-users point of view. Every user has his own preferences, which are represented by fuzzy functions and aggregation function. This thesis designs and implements several solutions of searching the best k objects when attributes data are stored on remote servers. It was necessary to modificate existing algorithms for this type of obtaining data. This thesis uses several variants of Fagin algorithm, indexing methods using B+ trees and communication via web services.
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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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Recommendation System for e-commerce
KORTUS, Lukáš
Based on the needs of the user, who decides between many products or services and does not want to deal with the passing of information available on all of these objects, there is a need for the recommendation systems that try to offer objects that would be for him to be interesting. Objective is therefore to propose appropriate ways of processing information about behavior of customers on the server and then design and implement a recommendation system that will use such processed information about the behavior of customers and will provide recommendations for users seeking information on the server of this e-commerce. In the theoretical part, we learn information about user?s preferences and recommender systems. In the practical part we propose module for e-commerce, which is not limited to the cold start problem. Furthermore, we are concerned with the problem of missing data. The accuracy of the solution to this problem then tested on the data of real users.
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