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Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie...

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Detalles Bibliográficos
Autores principales: Jayalakshmi, Sambandam, Ganesh, Narayanan, Čep, Robert, Senthil Murugan, Janakiraman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269752/
https://www.ncbi.nlm.nih.gov/pubmed/35808398
http://dx.doi.org/10.3390/s22134904
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author Jayalakshmi, Sambandam
Ganesh, Narayanan
Čep, Robert
Senthil Murugan, Janakiraman
author_facet Jayalakshmi, Sambandam
Ganesh, Narayanan
Čep, Robert
Senthil Murugan, Janakiraman
author_sort Jayalakshmi, Sambandam
collection PubMed
description Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.
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spelling pubmed-92697522022-07-09 Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions Jayalakshmi, Sambandam Ganesh, Narayanan Čep, Robert Senthil Murugan, Janakiraman Sensors (Basel) Article Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems. MDPI 2022-06-29 /pmc/articles/PMC9269752/ /pubmed/35808398 http://dx.doi.org/10.3390/s22134904 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jayalakshmi, Sambandam
Ganesh, Narayanan
Čep, Robert
Senthil Murugan, Janakiraman
Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions
title Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions
title_full Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions
title_fullStr Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions
title_full_unstemmed Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions
title_short Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions
title_sort movie recommender systems: concepts, methods, challenges, and future directions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269752/
https://www.ncbi.nlm.nih.gov/pubmed/35808398
http://dx.doi.org/10.3390/s22134904
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