Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/3503
Title: A neuroevolutionary neural network-based collaborative filtering recommendation system
Authors: Monemian, Seyedamin
Keywords: NeuroEvolution,;neural network;neuroevolution of augmenting;topologies (NEAT);recommendation systems
Issue Date: 29-May-2020
Abstract: The 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 The Fixed Topology NeuroEvolution approach is considered outdated when compared with an automated method for optimizing neural network structures.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.
URI: https://zone.biblio.laurentian.ca/handle/10219/3503
Appears in Collections:Master's theses

Files in This Item:
File Description SizeFormat 
S Monemian Thesis Final.pdf1.03 MBAdobe PDFThumbnail
View/Open


Items in LU|ZONE|UL are protected by copyright, with all rights reserved, unless otherwise indicated.