Predictive Accuracy of Machine Learning Algorithms in Recommender Systems

This work presents a systematic literature review on the application of Machine Learning algorithms in the development of effective movie recommender systems. With the increasing popularity of movie recommender systems in the entertainment industry, selecting appropriate algorithms has become crucia...

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Autor principal: Dumón, Marcos
Otros Autores: Gómez, Leticia Irene
Formato: Trabajo final de especialización
Lenguaje:Inglés
Publicado: 2019
Materias:
APRENDIZAJE AUTOMATICO
ALGORITMOS
SISTEMAS DE RECOMENDACION
INTELIGENCIA ARTIFICIAL
Acceso en línea:http://ri.itba.edu.ar/handle/20.500.14769/1834
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-1834
record_format dspace
spelling I32-R138-20.500.14769-18342026-01-15T15:07:27Z Predictive Accuracy of Machine Learning Algorithms in Recommender Systems Dumón, Marcos Gómez, Leticia Irene APRENDIZAJE AUTOMATICO ALGORITMOS SISTEMAS DE RECOMENDACION INTELIGENCIA ARTIFICIAL This work presents a systematic literature review on the application of Machine Learning algorithms in the development of effective movie recommender systems. With the increasing popularity of movie recommender systems in the entertainment industry, selecting appropriate algorithms has become crucial for delivering personalized and accurate recommendations to users. Through an extensive literature search and rigorous methodology, this work identifies and analyzes commonly used Machine Learning algorithms for movie recommendation. The accuracy and performance of these algorithms are evaluated using established evaluation methods and metrics on movie datasets of different sizes. The evaluation takes into account factors such as prediction accuracy, scalability, and robustness. The comparative analysis provides valuable insights into the effectiveness of various Machine Learning algorithms in the context of movie recommendation. The findings contribute to the understanding of algorithmic performance, enabling researchers and practitioners to make informed decisions when developing movie recommender systems. Additionally, the work explores the impact of different hyperparameters and optimization techniques on algorithm performance. The results of this work aim to improve the quality of movie recommendations and enhance user satisfaction. By providing guidelines and recommendations for algorithm selection and optimization, this work contributes to the advancement of movie recommender systems and the overall movie-watching experience. Trabajo Final Ciencia de Datos (especialización) - Instituto Tecnológico de Buenos Aires, Buenos Aires, 2019 2019-12-04T11:33:12Z 2019-12-04T11:33:12Z 2019 Trabajo final de especialización http://ri.itba.edu.ar/handle/20.500.14769/1834 en application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic APRENDIZAJE AUTOMATICO
ALGORITMOS
SISTEMAS DE RECOMENDACION
INTELIGENCIA ARTIFICIAL
spellingShingle APRENDIZAJE AUTOMATICO
ALGORITMOS
SISTEMAS DE RECOMENDACION
INTELIGENCIA ARTIFICIAL
Dumón, Marcos
Predictive Accuracy of Machine Learning Algorithms in Recommender Systems
topic_facet APRENDIZAJE AUTOMATICO
ALGORITMOS
SISTEMAS DE RECOMENDACION
INTELIGENCIA ARTIFICIAL
description This work presents a systematic literature review on the application of Machine Learning algorithms in the development of effective movie recommender systems. With the increasing popularity of movie recommender systems in the entertainment industry, selecting appropriate algorithms has become crucial for delivering personalized and accurate recommendations to users. Through an extensive literature search and rigorous methodology, this work identifies and analyzes commonly used Machine Learning algorithms for movie recommendation. The accuracy and performance of these algorithms are evaluated using established evaluation methods and metrics on movie datasets of different sizes. The evaluation takes into account factors such as prediction accuracy, scalability, and robustness. The comparative analysis provides valuable insights into the effectiveness of various Machine Learning algorithms in the context of movie recommendation. The findings contribute to the understanding of algorithmic performance, enabling researchers and practitioners to make informed decisions when developing movie recommender systems. Additionally, the work explores the impact of different hyperparameters and optimization techniques on algorithm performance. The results of this work aim to improve the quality of movie recommendations and enhance user satisfaction. By providing guidelines and recommendations for algorithm selection and optimization, this work contributes to the advancement of movie recommender systems and the overall movie-watching experience.
author2 Gómez, Leticia Irene
author_facet Gómez, Leticia Irene
Dumón, Marcos
format Trabajo final de especialización
author Dumón, Marcos
author_sort Dumón, Marcos
title Predictive Accuracy of Machine Learning Algorithms in Recommender Systems
title_short Predictive Accuracy of Machine Learning Algorithms in Recommender Systems
title_full Predictive Accuracy of Machine Learning Algorithms in Recommender Systems
title_fullStr Predictive Accuracy of Machine Learning Algorithms in Recommender Systems
title_full_unstemmed Predictive Accuracy of Machine Learning Algorithms in Recommender Systems
title_sort predictive accuracy of machine learning algorithms in recommender systems
publishDate 2019
url http://ri.itba.edu.ar/handle/20.500.14769/1834
work_keys_str_mv AT dumonmarcos predictiveaccuracyofmachinelearningalgorithmsinrecommendersystems
_version_ 1865139163778514944

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