Movie Recommendation System: Content-Based and Collaborative Filtering
DOI:
https://doi.org/10.26438/ijcse/v6i4.476481Keywords:
Content-Based Filtering, Collaborative Filtering, Movie RecommendationAbstract
Since last decade a huge amount of information is transferred over the internet on day to day basis. However, all the information is not relevant to each user and is also difficult to find the right content for the user as per his/her need. Recommender system works as a guide to find or suggest right items for users. A movie recommendation system is predicting or suggest a movie which user might like using his/her previous watch list or history. After Netflix prize competition many academician and researchers have shown interest to develop new and better filtering techniques for the movie recommendation. This paper studies the two most fundamental techniques: content-based and collaborative filtering methods of information retrieval and shows their application for movie recommendation with pros and cons. An experiment was carried out over MovieLens 100K dataset to show the implementation of discussed methods. The obtained results have shown that Item-Item based neighbourhood collaborative filtering method is better among implemented three techniques with 0.786 MAE and 0.985 RMSE values.
References
G. Adomavicius and a Tuzhilin, “Toward the Next Generation of Recommender Systems: a Survey of the State of the Art and Possible Extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005.
G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Comput., vol. 7, no. 1, pp. 76–80, 2003.
B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” Proc. tenth Int. Conf. World Wide Web - WWW ’01, pp. 285–295, 2001.
J. Zhang, Y. Lin, M. Lin, and J. Liu, “An effective collaborative filtering algorithm based on user preference clustering,” Appl. Intell., vol. 45, no. 2, pp. 230–240, 2016.
B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl, Application of Dimensionality Reduction in Recommender System - A Case Study, vol. 1625. 2000.
C. C. Aggarwal, “Content-Based Recommender Systems,” in Recommender Systems, 2016, pp. 139–166.
F. Cacheda, V. Carneiro, D. Fernández, and V. Formoso, “Comparison of collaborative filtering algorithms,” ACM Trans. Web, vol. 5, no. 1, pp. 1–33, 2011.
J. Bobadilla, F. Ortega, and A. Hernando, “A collaborative filtering similarity measure based on singularities,” Inf. Process. Manag., vol. 48, no. 2, pp. 204–217, 2012.
F. O. Isinkaye, Y. O. Folajimi, and B. A. Ojokoh, “Recommendation systems: Principles, methods and evaluation,” Egypt. Informatics J., vol. 16, no. 3, pp. 261–273, 2015.
X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell., vol. 2009, no. Section 3, pp. 1–19, 2009.
F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and Context,” ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, p. 19:1--19:19, 2015.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
