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Recommending Movies Using User-Based and Item-Based Collaborative Filtering on R Platform
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Recommender systems are information filtering systems that deal with the problem of information overload by filtering vital information fragment out of a large amount of dynamically generated information according to user’s preferences, interest or observed behaviour about an item. Recommender system has the ability to predict whether a particular user would prefer an item or not based on the user’s profile. Recommendation algorithms are best known for their use on e-commerce websites, where they use inputs about a customer’s interests to generate a list of recommended items. A new emerging sector in India, online movie viewership and subscription of movies, demands an expert and highly technical skill to understand the movie preferences of the viewers spread across India. This paper uses two techniques (run on R platform), user-based collaborative filtering and item-based collaborative filtering, to understand the preferences of people (without giving any reason to it) and recommending mechanism was solely based upon user-user similarity matrix and item-item similarity matrix. A dataset of 563 movies and 9,985 consumers from Amazon prime has been taken for recommending movies for people who have not watched a particular set of movies. The robustness of the two techniques is also compared and explained.
Keywords
User-Based Collaborative Filtering, Item-Based Collaborative Filtering, Recommendation Engine.
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- Alspector, J., Kolez, A., & Karunanithi, N. (1998). Comparing feature-based and clique-based user models for movie selection. In Proceedings of the Third ACM Conference on Digital Libraries, Pittsburgh PA, ACM Press, New York.
- Ang, I. (1996). Living room wars. Routledge, London.
- Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. Journal of Marketing Research, 37(3), 363-375.
- Ardissono, L., Gena, C., Torasso, P., Bellifemine, F., Difino, A., & Negro, B. (2004). User modeling and recommendation techniques for personalized electronic program guides. In L. Ardissono, A.
- Kobsa & M. Maybury (Eds.), Personalized Digital Television: Targeting Programs to Individual Viewers (pp. 3-26). Kluwer Academic Publishers, Dordrecht.
- Balabanovic, M., & Shoham, Y. (1997). FAB: Contentbased collaborative recommender. Communications of the ACM, 40(3), 66-72.
- Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. In Recommender System Workshop, 98, 11-15.
- Baudisch, P., Brueckner, L., & Scout, T. V. (2002). Guiding Users from Printed TV Program Guides to Personalized TV Recommendation. In Proceedings of the 2nd Workshop on Personalization in Future TV, (pp. 157-166). Malaga, Spain.
- Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User Adapted Interaction, 12(4), 331-370.
- Frank, R., & Greenberg, M. (1980). The public’s use of television: Who watches what and why. Sage, Beverly Hills.
- Gena, C., & Ardissono, L. (2001). On the Construction of TV Viewer Stereotypes Starting from Lifestyle Surveys. In Workshop on Personalization in Future TV, Sonthofen, Germany.
- Hsu, H. S., Wen, M. H., & Lee, C. H. (2006). An ActivityOriented Approach to Designing a User Interface for Digital Television. In Proceedings of the 4th Euro iTV Conference, Athens, Greece, (pp. 83-90).
- Wang, J., Polwelse, J., Fokker, J., & Reinders, M. J. T. (2006). Personalization of a Peer-to-Peer Television System. In Proceedings of the 4th EuroiTV Conference, Athens, Greece, (pp. 147-155).
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