Netflix Movie Recommendation Using Fuzzy Logic

A movie as a means of entertainment presents a great variety of characteristics and attributes such as: genre, rating, languages, locations, theme, reviews, special effects, etc. In recent years, with the advent of a large number of streaming services for movies and series, the catalog of options to...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hugo David Calderon Vilca, Valerie A. Namuche Zavala, Marco A. Herrera Vargas
Formato:	article
Lenguaje:	EN
Publicado:	FRUCT 2021
Materias:	fuzzy logic expert system movie recommendation Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/0183bdd3fd60462ab4faa2a33bc923e3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0183bdd3fd60462ab4faa2a33bc923e3
record_format	dspace
spelling	oai:doaj.org-article:0183bdd3fd60462ab4faa2a33bc923e32021-11-20T15:59:33ZNetflix Movie Recommendation Using Fuzzy Logic2305-72542343-073710.5281/zenodo.5639761https://doaj.org/article/0183bdd3fd60462ab4faa2a33bc923e32021-10-01T00:00:00Zhttps://www.fruct.org/publications/acm30/files/Cal3.pdfhttps://doaj.org/toc/2305-7254https://doaj.org/toc/2343-0737A movie as a means of entertainment presents a great variety of characteristics and attributes such as: genre, rating, languages, locations, theme, reviews, special effects, etc. In recent years, with the advent of a large number of streaming services for movies and series, the catalog of options to choose from has increased, which can be a problem for the vast majority of people. In the present research we propose movie recommendation using fuzzy logic. Two databases were used with which we were able to retrieve approximately 1000 movies which are on the Netflix platform. From these movies we took four attributes (Audience Score, Critic Score, Audience Count and Year) with which we have designed 19 fuzzy logic inference rules calculating in this way the probability of movie recommendation, then we filtered according to user data: age, preferred movie length, year of the movie production, genre, and language of the movie, finally with the attributes and data provided by the user se managed to recommend a list of movies. As a result of the experiment, we show that our proposal reaches an accuracy percentage of 83% on the comments made by a set of users who used it.Hugo David Calderon VilcaValerie A. Namuche ZavalaMarco A. Herrera VargasFRUCTarticlefuzzy logicexpert systemmovie recommendationTelecommunicationTK5101-6720ENProceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 30, Iss 2, Pp 331-337 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuzzy logic expert system movie recommendation Telecommunication TK5101-6720
spellingShingle	fuzzy logic expert system movie recommendation Telecommunication TK5101-6720 Hugo David Calderon Vilca Valerie A. Namuche Zavala Marco A. Herrera Vargas Netflix Movie Recommendation Using Fuzzy Logic
description	A movie as a means of entertainment presents a great variety of characteristics and attributes such as: genre, rating, languages, locations, theme, reviews, special effects, etc. In recent years, with the advent of a large number of streaming services for movies and series, the catalog of options to choose from has increased, which can be a problem for the vast majority of people. In the present research we propose movie recommendation using fuzzy logic. Two databases were used with which we were able to retrieve approximately 1000 movies which are on the Netflix platform. From these movies we took four attributes (Audience Score, Critic Score, Audience Count and Year) with which we have designed 19 fuzzy logic inference rules calculating in this way the probability of movie recommendation, then we filtered according to user data: age, preferred movie length, year of the movie production, genre, and language of the movie, finally with the attributes and data provided by the user se managed to recommend a list of movies. As a result of the experiment, we show that our proposal reaches an accuracy percentage of 83% on the comments made by a set of users who used it.
format	article
author	Hugo David Calderon Vilca Valerie A. Namuche Zavala Marco A. Herrera Vargas
author_facet	Hugo David Calderon Vilca Valerie A. Namuche Zavala Marco A. Herrera Vargas
author_sort	Hugo David Calderon Vilca
title	Netflix Movie Recommendation Using Fuzzy Logic
title_short	Netflix Movie Recommendation Using Fuzzy Logic
title_full	Netflix Movie Recommendation Using Fuzzy Logic
title_fullStr	Netflix Movie Recommendation Using Fuzzy Logic
title_full_unstemmed	Netflix Movie Recommendation Using Fuzzy Logic
title_sort	netflix movie recommendation using fuzzy logic
publisher	FRUCT
publishDate	2021
url	https://doaj.org/article/0183bdd3fd60462ab4faa2a33bc923e3
work_keys_str_mv	AT hugodavidcalderonvilca netflixmovierecommendationusingfuzzylogic AT valerieanamuchezavala netflixmovierecommendationusingfuzzylogic AT marcoaherreravargas netflixmovierecommendationusingfuzzylogic
_version_	1718419402870751232

Netflix Movie Recommendation Using Fuzzy Logic

Ejemplares similares