A Movie Recommender System Based on Topic Modeling using Machine Learning Methods
In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a s...
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| Format: | Article |
| Language: | English |
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University of science and culture
2022-07-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_164092_06a193e69311f81adb3b070763733cc5.pdf |
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| author | Mojtaba Kordabadi Amin Nazari Muharram Mansoorizadeh |
| author_facet | Mojtaba Kordabadi Amin Nazari Muharram Mansoorizadeh |
| author_sort | Mojtaba Kordabadi |
| collection | DOAJ |
| description | In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user's age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user's age and recommend movies based on the user's age with 93% accuracy, which is highly satisfactory. |
| format | Article |
| id | doaj-art-401799540ed6465da03c4e51e1ac25b5 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-401799540ed6465da03c4e51e1ac25b52025-01-04T09:28:50ZengUniversity of science and cultureInternational Journal of Web Research2645-43432022-07-0152192810.22133/ijwr.2022.370251.1139A Movie Recommender System Based on Topic Modeling using Machine Learning MethodsMojtaba Kordabadi0Amin Nazari1Muharram Mansoorizadeh2https://orcid.org/0000-0002-7131-1047MSc, Computer Engineering Department, Bu-Ali Sina University, Hamedan, IranPh.D. Candidate, Computer Engineering Department, Bu-Ali Sina University, Hamedan, IranAssociate Professor, Computer Engineering Department, Bu-Ali Sina UniversityIn recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user's age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user's age and recommend movies based on the user's age with 93% accuracy, which is highly satisfactory.https://ijwr.usc.ac.ir/article_164092_06a193e69311f81adb3b070763733cc5.pdfrecommendation systemstext classificationtopic modeling |
| spellingShingle | Mojtaba Kordabadi Amin Nazari Muharram Mansoorizadeh A Movie Recommender System Based on Topic Modeling using Machine Learning Methods International Journal of Web Research recommendation systems text classification topic modeling |
| title | A Movie Recommender System Based on Topic Modeling using Machine Learning Methods |
| title_full | A Movie Recommender System Based on Topic Modeling using Machine Learning Methods |
| title_fullStr | A Movie Recommender System Based on Topic Modeling using Machine Learning Methods |
| title_full_unstemmed | A Movie Recommender System Based on Topic Modeling using Machine Learning Methods |
| title_short | A Movie Recommender System Based on Topic Modeling using Machine Learning Methods |
| title_sort | movie recommender system based on topic modeling using machine learning methods |
| topic | recommendation systems text classification topic modeling |
| url | https://ijwr.usc.ac.ir/article_164092_06a193e69311f81adb3b070763733cc5.pdf |
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