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...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
FRUCT
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/0183bdd3fd60462ab4faa2a33bc923e3 |
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| Sumario: | 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. |
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