Please use this identifier to cite or link to this item:
Title: Recommending media content based on machine learning methods
Authors: Dias, Pedro Ricardo Gomes
Orientador: Magalhães, João
Keywords: Recommender systems
Collaborative filtering
Matrix factorization
Groupbased recommendations
Interactive TV
Issue Date: 2011
Publisher: Faculdade de Ciências e Tecnologia
Abstract: Information is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with the heavy burden of choosing what is relevant. With the increasing growth of media sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing personalized and meaningful content recommendations based on users’ preferences and usage history. Due to their utility and commercial potential, recommender systems integrate many audiovisual content providers and represent one of their most important and valuable services. The goal of this thesis is to develop a recommender system based on matrix factorization methods, capable of providing meaningful and personalized product recommendations to individual users and groups of users, by taking into account users’ rating patterns and biased tendencies, as well as their fluctuations throughout time.
Description: Dissertação para obtenção do Grau de Mestre em Engenharia Informática
Appears in Collections:FCT: DI - Dissertações de Mestrado

Files in This Item:
File Description SizeFormat 
Dias_2011.pdf32,9 MBAdobe PDFView/Open

FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.