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Intelligent and Converged Networks 2023, 4(4): 296-304 https://doi.org/10.23919/ICN.2023.0024
Open Access | Issue | Published: 30 December 2023

Design and analysis of a recommendation system based on collaborative filtering techniques for big data

Show Author's Information Najia Khouibiri1,  Yousef Farhaoui1( ),  Ahmad El Allaoui1
STI Laboratory, IDM, T-IDMS, Faculty of Sciences and Techniques Errachidia, Moulay Ismail University, Meknes 5003, Morocco
Keywords:
big data, machine learning, recommendation system, collaborative filtering (CF), decision support system
Cite this article:
Khouibiri N, Farhaoui Y, El Allaoui A. Design and analysis of a recommendation system based on collaborative filtering techniques for big data. Intelligent and Converged Networks, 2023, 4(4): 296-304. https://doi.org/10.23919/ICN.2023.0024
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Abstract Full text About this article

Online search has become very popular, and users can easily search for any movie title; however, to easily search for moving titles, users have to select a title that suits their taste. Otherwise, people will have difficulty choosing the film they want to watch. The process of choosing or searching for a film in a large film database is currently time-consuming and tedious. Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste. This happens especially because humans are confused about choosing things and quickly change their minds. Hence, the recommendation system becomes critical. This study aims to reduce user effort and facilitate the movie research task. Further, we used the root mean square error scale to evaluate and compare different models adopted in this paper. These models were employed with the aim of developing a classification model for predicting movies. Thus, we tested and evaluated several cooperative filtering techniques. We used four approaches to implement sparse matrix completion algorithms: k-nearest neighbors, matrix factorization, co-clustering, and slope-one.


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About this article

Design and analysis of a recommendation system based on collaborative filtering techniques for big data

Show Author's information Najia Khouibiri1, Yousef Farhaoui1( ), Ahmad El Allaoui1
STI Laboratory, IDM, T-IDMS, Faculty of Sciences and Techniques Errachidia, Moulay Ismail University, Meknes 5003, Morocco

Abstract

Online search has become very popular, and users can easily search for any movie title; however, to easily search for moving titles, users have to select a title that suits their taste. Otherwise, people will have difficulty choosing the film they want to watch. The process of choosing or searching for a film in a large film database is currently time-consuming and tedious. Users spend extensive time on the internet or on several movie viewing sites without success until they find a film that matches their taste. This happens especially because humans are confused about choosing things and quickly change their minds. Hence, the recommendation system becomes critical. This study aims to reduce user effort and facilitate the movie research task. Further, we used the root mean square error scale to evaluate and compare different models adopted in this paper. These models were employed with the aim of developing a classification model for predicting movies. Thus, we tested and evaluated several cooperative filtering techniques. We used four approaches to implement sparse matrix completion algorithms: k-nearest neighbors, matrix factorization, co-clustering, and slope-one.

Keywords: big data, machine learning, recommendation system, collaborative filtering (CF), decision support system

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Publication history

Received: 25 April 2023
Accepted: 21 June 2023
Published: 30 December 2023
Issue date: December 2023

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This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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