Content based movie recommendation system
Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ayandegan Institute of Higher Education,
2020-12-01
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| Series: | International Journal of Research in Industrial Engineering |
| Subjects: | |
| Online Access: | http://www.riejournal.com/article_121501_a3717e6cf19a1845e350acb9148751ee.pdf |
| _version_ | 1818606473913040896 |
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| author | N. Pradeep K. K. Rao Mangalore B. Rajpal N. Prasad R. Shastri |
| author_facet | N. Pradeep K. K. Rao Mangalore B. Rajpal N. Prasad R. Shastri |
| author_sort | N. Pradeep |
| collection | DOAJ |
| description | Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies from the thousand already existing ones. Content-based recommendation system describes the items that may be recommended to the user. Based on a data set, it predicts what movies a user will like considering the attributes present in the previously liked movies. Recommendation systems can recommend movies based on one or a combination of two or more attributes. While designing a movie recommendation system various factors are considered such as the genre of the movie, the director or the actors present in it. In this paper, the recommendation system has been built on cast, keywords, crew, and genres. A single column is created which will be the sum of all the 4 attributes, and it acts as a dominant factor for this movie recommender system. |
| first_indexed | 2024-12-16T14:11:25Z |
| format | Article |
| id | doaj.art-45884755ff3748f282958365b04a664d |
| institution | Directory Open Access Journal |
| issn | 2783-1337 2717-2937 |
| language | English |
| last_indexed | 2024-12-16T14:11:25Z |
| publishDate | 2020-12-01 |
| publisher | Ayandegan Institute of Higher Education, |
| record_format | Article |
| series | International Journal of Research in Industrial Engineering |
| spelling | doaj.art-45884755ff3748f282958365b04a664d2022-12-21T22:28:45ZengAyandegan Institute of Higher Education,International Journal of Research in Industrial Engineering2783-13372717-29372020-12-019433734810.22105/riej.2020.259302.1156121501Content based movie recommendation systemN. Pradeep0K. K. Rao Mangalore1B. Rajpal2N. Prasad3R. Shastri4Department of MCA ,School of Computer Science &IT ,Jain (deemed-to-be) University ,Bengaluru, India.Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.Department of MCA, School of Computer Science and IT, Jain (deemed-to-be) University, Bengaluru, India.Recommendation based systems can be used for recommending different web page, books, restaurants, tv shows, movies etc. The aim of movie recommendation system is to recommend movies to different users based on their interests. This helps the user to save time browsing the internet looking for movies from the thousand already existing ones. Content-based recommendation system describes the items that may be recommended to the user. Based on a data set, it predicts what movies a user will like considering the attributes present in the previously liked movies. Recommendation systems can recommend movies based on one or a combination of two or more attributes. While designing a movie recommendation system various factors are considered such as the genre of the movie, the director or the actors present in it. In this paper, the recommendation system has been built on cast, keywords, crew, and genres. A single column is created which will be the sum of all the 4 attributes, and it acts as a dominant factor for this movie recommender system.http://www.riejournal.com/article_121501_a3717e6cf19a1845e350acb9148751ee.pdfcontent based recommendationpycharmpythonmachine learningweb application |
| spellingShingle | N. Pradeep K. K. Rao Mangalore B. Rajpal N. Prasad R. Shastri Content based movie recommendation system International Journal of Research in Industrial Engineering content based recommendation pycharm python machine learning web application |
| title | Content based movie recommendation system |
| title_full | Content based movie recommendation system |
| title_fullStr | Content based movie recommendation system |
| title_full_unstemmed | Content based movie recommendation system |
| title_short | Content based movie recommendation system |
| title_sort | content based movie recommendation system |
| topic | content based recommendation pycharm python machine learning web application |
| url | http://www.riejournal.com/article_121501_a3717e6cf19a1845e350acb9148751ee.pdf |
| work_keys_str_mv | AT npradeep contentbasedmovierecommendationsystem AT kkraomangalore contentbasedmovierecommendationsystem AT brajpal contentbasedmovierecommendationsystem AT nprasad contentbasedmovierecommendationsystem AT rshastri contentbasedmovierecommendationsystem |