Abstract
Movie recommendation has become one of the most efficient ways of making the user experience more personalized and connecting the user with the movies that the user might like. In this paper, FindMoviez, a movie recommendation engine has been introduced, which is based on a combination of two recommendation algorithms, implemented in a web application. The three sub-data sets (i.e.m ratings, users, and metadata) of the famous Movielens data set have been used. A combination of item–item collaborative filtering and genre based using the average weighted rating method has been used. These algorithms have been modified in a way where the user is always recommended movies, and even if one of the above algorithms fails, the other comes into play making this product more reliable for the user. Thus, the user can completely relies on this product for genuine movie recommendations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Garanayak, M., Mohanty, S.N., Jagadevb, A.K., Sahoo, S.: Recommender system using item based collaborative filtering (CF) and K-means. Int. J. Knowl. Based Intell. Eng. Syst. 23, 93 (2019)
2011 Internet Use Survey Summary Report, KISA, pp. 2–27 (2011)
Jeong, W.-h., Kim, S-j., Park, D.-s., Kwak, J.: Performance improvement of a movie recommendation system based on personal propensity and secure collaborative filtering. J. Inf. Process Syst. 9(1), 157 (2013)
Manvi, S.S., Nalini, N., Bhajantri, L.B.: Recommender system in ubiqui tous commerce. In: International Conference on Electronics Computer Technology, IEEE, pp. 434–438 (2011)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceeding of the SIGCHI Conference on Human Factors in Computing Systems, pp. 194–201 (1995)
Resnick, P., Lacovou, N., Sushak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of Netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, NC, pp. 175–186 (2001)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating ‘word of mouth’. In: Proceedings of the SIGCHI Conference on Human factors in Computing Systems, pp. 210–217 (1995)
Konstan, J.A., Ried, J.: Recommendation systems: from algorithms to user experience. User Model User-Adapt Inter. 22, 101–123 (2012)
Pan, C., Li, W.: Research paper recommendation with topic analysis. Comput. Des. Appl. IEEE 2, v4-246 (2010)
Ben Schafer, J., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems, pp. V9.1-291. Department of Computer Science, University of Northern Iowa (2007)
McSherry, D.: Explaining the pros and cons of conclusions in CBR. In: European Conference on Case based Reasoning, pp. 317–330. Springer, Berlin (2004)
Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–67 (1997)
Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online news paper. In: Proceedings of ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA (1999)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J: Item-Based Collaborative Filtering Recommendation Algorithms, pp. v3-288. GroupLens Research Group/Army HPC Research Center Department of Computer Science and Engineering University of Minnesota, Minneapolis, MN 55455
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M., Combining content-based and collaborative filters in an online news paper. In: Proceedings of ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Padhi, A.K., Mohanty, A., Sahoo, S. (2021). FindMoviez: A Movie Recommendation System. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_5
Download citation
DOI: https://doi.org/10.1007/978-981-33-6081-5_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6080-8
Online ISBN: 978-981-33-6081-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)