Movie Recommendation System Using Item Based Collaborative Filtering
Keywords:
Movies, Recommendation system, CBF Content-based filtering, CF- Collaborative filteringAbstract
In today's digital world where there is an endless variety of content consumed such as books, videos, articles, Films, etc., finding material of one's choice has become an infallible task. Digital content on the other hand Providers want to engage more and more users in their service for maximum time. Where is it the recommender system comes into picture where content providers advise users by content User choice in this paper we have proposed a movie recommendation system .Purpose of movie recommendation system aims to provide users with accurate movie recommendations. Usually basic recommendation system to make recommendations consider one of the following factors; User preference known as content based Filtering or the preference of similar users known as collaborative filtering. To create a stable and accurate recommender system will use of content based filtering.
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References
James Bennett, Stan Lanning ; “The Netflix Prize”, In KDD Cup and Workshop in conjunction with KDD,2007 [2] Mohammad Yahya H. Al-Shamri , Kamal K.
Bharadwaj; “A Compact User Model for Hybrid Movie Recommender System ” in International Conference on Computational Intelligence and Multimedia Applications 2007
Costin-Gabriel Chiru, Vladimir-Nicolae Dinu , Ctlina Preda, Matei Macri ; “Movie Recommender System Using the User's Psychological Profile” in IEEE International Conference on ICCP, 2015.
Christina Christakou, Leonidas Lefakis, Spyros Vrettos and Andreas Stafylopatis; “A Movie Recommender System Based on Semi-supervised Clustering ”, IEEE Computer Society Washington, DC, USA 2015.
Luis M. de Campos, Juan M. Fernández-Luna *, Juan F. Huete, Miguel A. Rueda-Morales; “Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks”, International Journal of Approximate Reasoning, revised 2010.
Urszula Kużelewska; “Clustering Algorithms in Hybrid Recommender System on MovieLens Data”, Studies in Logic, Grammar and Rhetoric, 2014.
Dietmar Jannach, Gerhard Friedrich; “Tutorial: Recommender Systems”, International Joint Conference on Artificial Intelligence, Beijing, August 4, 2013.
Gaurangi, Eyrun, Nan; “MovieGEN: A Movie Recommendation System”, UCSB.
Harpreet Kaur Virk, Er. Maninder Singh,” Analysis and Design of Hybrid Online Movie Recommender System ”International Journal of Innovations in Engineering and Technology (IJIET)Volume 5 Issue 2,April 2015.
Manoj Kumar, D.KYadav, Ankur Singh, Vijay Kr. Gupta,” A Movie Recommender System: MOVREC” International Journal of Computer Applications (0975 – 8887) Volume 124 – No.3, August 2015.
Prerana Khurana , Shabnam Parveen; ‘Approaches of Recommender System: A Survey’; International Journal of Computer Trends and Technology (IJCTT) – Volume 34 Number 3 - April 2016.
Utkarsh Gupta1 and Dr Nagamma Patil2,” Recommender System Based on Hierarchical Clustering Algorithm Chameleon” 2015 IEEE International Advance Computing Conference(IACC).