Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?

Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?

One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. These dense matrices are then m...

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Bibliographic Details
Main Author: Strömqvist, Zakris
Format: Others
Language:English
Published: Uppsala universitet, Statistiska institutionen 2018
Subjects:
Recommender systems
Collaborative filtering
Matrix factorization
Probability Theory and Statistics
Sannolikhetsteori och statistik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653
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spelling ndltd-UPSALLA1-oai-DiVA.org-uu-3526532018-06-20T05:56:29ZMatrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?engStrömqvist, ZakrisUppsala universitet, Statistiska institutionen2018Recommender systemsCollaborative filteringMatrix factorizationProbability Theory and StatisticsSannolikhetsteori och statistikOne of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. These dense matrices are then made sparse in two different ways to simulate different kinds of data. The accuracy of MF is then measured on each of the simulated sparse matrices. This shows that the matrix factorization models are sensitive to the degree of information available. For high levels of sparsity the MF performs badly but as the information level increases the accuracy of the models improve, for both samples. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Recommender systems
Collaborative filtering
Matrix factorization
Probability Theory and Statistics
Sannolikhetsteori och statistik
spellingShingle Recommender systems
Collaborative filtering
Matrix factorization
Probability Theory and Statistics
Sannolikhetsteori och statistik
Strömqvist, Zakris
Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?
description One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. These dense matrices are then made sparse in two different ways to simulate different kinds of data. The accuracy of MF is then measured on each of the simulated sparse matrices. This shows that the matrix factorization models are sensitive to the degree of information available. For high levels of sparsity the MF performs badly but as the information level increases the accuracy of the models improve, for both samples.
author Strömqvist, Zakris
author_facet Strömqvist, Zakris
author_sort Strömqvist, Zakris
title Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?
title_short Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?
title_full Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?
title_fullStr Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?
title_full_unstemmed Matrix factorization in recommender systems : How sensitive are matrix factorization models to sparsity?
title_sort matrix factorization in recommender systems : how sensitive are matrix factorization models to sparsity?
publisher Uppsala universitet, Statistiska institutionen
publishDate 2018
url http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352653
work_keys_str_mv AT stromqvistzakris matrixfactorizationinrecommendersystemshowsensitivearematrixfactorizationmodelstosparsity
_version_ 1718697886899765248

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