Application of imputation techniques in collaborative filtering-based recommender systems
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 importa...
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| Formato: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/83565 |
| Acesso em linha: | http://hdl.handle.net/10609/83565 |
| Access Level: | acceso abierto |
| Palavra-chave: | imputation methods recommender systems multivariate analysis mètode d'imputació sistemes de recomanació anàlisi multivariada método de imputación sistemas de recomendación análisis multivariado Computer algorithms -- TFM Algorismes computacionals -- TFM Algoritmos computacionales -- TFM |
| Resumo: | 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. |
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