Abstract
The movie recommendation system is a hybrid filtering system that performs both collaborative and content-based filtering of data to provide recommendations to users regarding movies. The system conforms to a different approach where it seeks the similarity of users among others clustered around the various genres and utilizes his preference of movies based on their content in terms of genres as the deciding factor of the recommendation of the movies to them. The system is based on the belief that a user rates movies in a similar fashion to other users that harbor the same state as the current user and is also affected by the other activities (in terms of rating) he performs with other movies. It follows the hypothesis that a user can be accurately recommended media on the basis others interests (collaborative filtering) and the movies themselves (content-based filtering).
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References
Michael Fleischman, Eduard Hovy. “Recommendations without User Preferences: A Natural Language Processing Approach”. In proceedings of the 8th international conference on Intelligent user interfaces, pages 242–244. Miami, Florida, USA, 2003.
A. Saranya, A. Hussain. “User Genre Movie Recommendation System Using NB Tree”. In proceedings of the International Journal of Innovative Research in Science, Engineering and Technology. Vol. 4, Issue 7, July 2015.
Hans Byström. “Movie Recommendations from User Ratings”. Stanford University, 2013.
Eyrun A. Eyjolfsdottir, Gaurangi Tilak and Nan Li. MovieGEN: “A Movie Recommendation System”. Technical report, Computer Science Department, University of California Santa Barbara, 2010.
Debadrita Roy, Arnab Kundu. “Design of movie recommendation system by means of collaborative filtering”. Int. J. Emerg. Technol. and Adv. Eng, 2013.
Kumar, M., Yadav, D.K., Singh A., Gupta and V.K. “A movie recommender system: Movrec”. In International Journal of Computer Applications, ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 124, pp. 0975–8887, 2015.
Kaivan Wadia, Pulkit Gupta. “Movie Recommendation System based on Self-Organizing Maps”. The University of Texas at Austin, Austin, Texas, 2011.
Karzan Wakil, Rebwar Bakhtyar, Karwan Ali and Kozhin Alaadin. “Improving Web Movie Recommender System Based on Emotions”, International Journal of Advanced Computer Science and Applications, vol. 6, no. 2, 2015.
Roberto Mirizzi, Tommaso Di Noia, Azzurra Ragone. “Movie recommendation with dbpedia”. In CEUR Workshop Proceedings, vol. 835, 2012.
Wang Z, Yu X, Feng N and Wang Z. “An improved collaborative movie recommendation system using computational intelligence”. J Vis Lang Comput 25:667–675, 2014.
Rupali Hande, Ajinkya Gutti, Kevin Shah, Jeet Gandhi and Vrushal Kamtikar. “MOVIEMENDER - A MOVIE RECOMMENDER SYSTEM”, International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol. 5, No. 11), 2016.
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Jain, K.N., Kumar, V., Kumar, P., Choudhury, T. (2018). Movie Recommendation System: Hybrid Information Filtering System. In: Bhalla, S., Bhateja, V., Chandavale, A., Hiwale, A., Satapathy, S. (eds) Intelligent Computing and Information and Communication. Advances in Intelligent Systems and Computing, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-10-7245-1_66
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DOI: https://doi.org/10.1007/978-981-10-7245-1_66
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