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dc.contributor.authorMonemian, Seyedamin-
dc.description.abstractThe success of a neural network-based recommendation system depends on finding an architecture to fit the task. The Fixed Topology NeuroEvolution approach is considered outdated when compared with an automated method for optimizing neural network structures. A NeuroEvolutionary algorithm has been developed in order to enhance the structure of a neural network to yield a more accurate recommendation system based on some of the existing benchmarks for measuring recommendation capability. The genetic algorithm ensures us to gain a more efficient topology produced by different generations through each step of the evolution process. Results show that this method performs better than many other algorithms and is close to the Singular Value Decomposition based algorithm developed by Funk as a benchmark standard.en_US
dc.subjectneural networken_US
dc.subjectneuroevolution of augmentingen_US
dc.subjecttopologies (NEAT)en_US
dc.subjectrecommendation systemsen_US
dc.titleA neuroevolutionary neural network-based collaborative filtering recommendation systemen_US
dc.description.degreeMaster of Science (MSc) in Computational Scienceen_US
dc.publisher.grantorLaurentian University of Sudburyen_US
Appears in Collections:Computational Sciences - Master's theses
Master's Theses

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