Development of Online Movie Recommendation System based on Neighborhood-based Collaborative Filtering
Phan Thi Ha1, Trinh Thi Van Anh2
1Dr. Phan Thi Ha, Lecturer, Faculty of Information Technology, Posts and Telecommunications Institute of Technology (PTIT), Ha Noi, Vietnam, and Department of Computing Fundamental, FPT University, Hanoi, Vietnam.
2Trinh Thi Van Anh, Lecturer, Faculty of Information Technology, Posts and Telecommunications Institute of Technology (PTIT), Ha Noi, Vietnam, and Department of Computing Fundamental, FPT University, Hanoi, Vietnam.
Manuscript received on 08 June 2022 | Revised Manuscript received on 15 June 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 14-16 | Volume-11 Issue-8, July 2022 | Retrieval Number: 100.1/ijitee.H91260711822 | DOI: 10.35940/ijitee.H9126.0711822
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The recommendation system integrated in movie streaming provides relevant information to viewers predicted by viewers’ past behaviors. There are basically two methods, Content-Based Filtering and Collaborative Filtering. In this article, our focus is on the second method which is based on memory, namely Neighborhood-based Collaborative Filtering (NBCF), to make movie recommendations to users given users’ information. Simultaneously, we have built an online movie website and integrated the movie recommendation system based on NBCF to assist users in movie selection. In the process of building the website, apart from building diagram of movie recommendation system’s functions, class diagram of movie recommendation function, sequence diagram of movie recommendation function, we also build a user-recommended movie model based on the Movies Lens dataset for a fairly high accuracy, which is 99%.
Keywords: Neighborhood-based Collaborative Filtering (NBCF), Recommendation System, Item_KNN.
Scope of the Article: Collaborative applications