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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: | Rodriguez Velazquez, Juan Alberto |
Tutor: | Solanas, Agusti |
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. |
Keywords: | imputation methods recommender systems multivariate analysis |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jul-2018 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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sdiazromoTFM0618memoria.pdf | Memory of TFM | 4,92 MB | Adobe PDF | View/Open |
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