System based on Neighborhood-based Collaborative Filtering
Authors/Creators
- 1. Lecturer, Faculty of Information Technology, Posts and Telecommunications Institute of Technology (PTIT), Ha Noi, Vietnam, and Department of Computing Fundamental, FPT University, Hanoi, Vietnam.
Contributors
Contact person:
- 1. Lecturer, Faculty of Information Technology, Posts and Telecommunications Institute of Technology (PTIT), Ha Noi, Vietnam, and Department of Computing Fundamental, FPT University, Hanoi, Vietnam.
Description
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[9] dataset for a fairly high accuracy, which is 99%.
Notes
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Additional details
Related works
- Is cited by
- Journal article: 2278-3075 (ISSN)
References
- P. Mathew, B. Kuriakose and V. Hegde, "Book Recommendation System through content based and collaborative filtering method," 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 2016, pp. 47-52, doi: 10.1109/SAPIENCE.2016.7684166
- Aggarwal, C.C. (2016). Neighborhood-Based Collaborative Filtering. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_2
- Naresh E, Geetha LM, Vijaya Kumar BP; "Recommendation system and its approaches- A survey"; International Journal of Scientific & Engineering Research, Volume 7, Issue 5, May-2016
- F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh; "Recommendation systems: Principles, methods and evaluation"; Egyptian Informatics Journal (2015) 16, 261–273
- A. T¨oscher và M.Jahrer ,Collaborative Filtering Applied toEducational Data Mining , in Proceedings of the KDD Cup2010 Workshop on Improving CognitiveModels with Educational Data Mining,Washington, DC, USA, 2010
- N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe, and L. Schmidt-Thieme,"Recommender system for predictingstudent performance," in Proceedings ofthe ACM RecSys 2010 Workshop onRecommender Systems for TechnologyEnhanced Learning (RecSysTEL 2010),vol. 1. Elsevier's ProcediaComputerScience, 2010, pp. 2811 – 2819
- L. Herlocker, J. A. Konstan, et al., AnAlgorithmic Framework for PerformingCollaborative Filtering, Proceedings of the22nd Annual International ACM SIGIR Conference, ACM Press, 1999, pp. 230–237
- U. Shardanand and P. Maes, "Socialinformation filtering: algorithms forautomating 'word of mouth" inProceeding of the SIGCHI Conference onHuman Factors in Computing Systems, ser.CHI '95. New York, NY, USA: ACMPress/Addison-Wesley Publishing Co.,1995, pp. 210–217
- https://grouplens.org/datasets/movielens/100k/
Subjects
- ISSN: 2278-3075 (Online)
- https://portal.issn.org/resource/ISSN/2278-3075#
- Retrieval Number: 100.1/ijitee.H91260711822
- https://www.ijitee.org/portfolio-item/h91260711822/
- Journal Website: www.ijitee.org
- https://www.ijitee.org/
- Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
- https://www.blueeyesintelligence.org/