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http://hdl.handle.net/10609/83565
Title: Application of imputation techniques in collaborative filtering-based recommender systems
Author: Díaz Romo, Sandra
Director: Rodríguez Velázquez, Juan Alberto
Tutor: Solanas Gómez, Agustí
Keywords: imputation methods
recommender systems
multivariate analysis
Issue Date: Jul-2018
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: This master thesis focuses on imputation methods as an approach to deal with the missing data problem. It's common that big datasets contain missing values. However, their existence may suppose a problem, as most of the usual statistical techniques can't be used and they may shadow important features, leading to the extraction of incorrect conclusions. In this context, imputation methods are statistical methods used to infer the missing values of a dataset using its intrinsic properties and the correlation amongst their variables. Several of these methods have been studied in detail and used in a recommender systems case study.
Language: English
URI: http://hdl.handle.net/10609/83565
Appears in Collections:Bachelor thesis, research projects, etc.

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