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dc.contributor.author
Siomos, Theodosios
en
dc.date.accessioned
2019-04-19T12:30:48Z
dc.date.available
2019-04-20T00:00:23Z
dc.date.issued
2019-04-19
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29406
dc.rights
Default License
dc.subject
Basic recommendation algorithms
en
dc.title
Parallel Implementation of Basic Recommendation Algorithms
en
heal.type
masterThesis
en_US
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2019-04-20
heal.abstract
A recommender system is a powerful tool that improves customer’s experience through personalized recommendations. In order to provide recommendations, it analyzes the user behavior, such as the ratings available and browsing history. A well-designed recommender system is able to significantly increase the revenue of e-commerce web sites and applications. There are many different models of recommender systems implemented. A successful recommender system must be accurate in its predictions and fast at the same time. A satisfied customer is very likely to be loyal to the web site or application. For that reason, the different models must be evaluated in terms of accuracy and performance. The goal of this dissertation is to implement and parallelize three popular collaborative filtering methods. The methods implemented are the user-based collaborative filtering with the Pearson correlation, the item-based collaborative filtering with the adjusted cosine correlation and the model-based alternating least squares method. These methods are also going to be evaluated for their accuracy and performance. For that reason, a set of different experimental metrics is used to evaluate their computing performance and their ability to provide accurate predictions
en
heal.advisorName
Diamantaras, Konstantinos
en
heal.committeeMemberName
Diamantaras, Konstantinos
en
heal.committeeMemberName
Gatzianas, Marios
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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