Building a Movie Recommendation System using SVD algorithm
DOI:
https://doi.org/10.26438/ijcse/v6i11.727729Keywords:
Recommendation System, SVD Decomposition, Netflix, Dimensionality reductionAbstract
Recommendation System predicts or recommends a set of products or items based upon the preference of the user. Recommender systems are utilized in variety of areas including movies, music, news, books search queries in general. This paper focuses on the design and development of a movie recommendation system using the SVD (Singular Value Decomposition) algorithm where we see that how sparse data are in real life situation and thereby predefined strategies such as collaborative or content-based filtering cannot be applied to these data for better results. Our objective is to reduce the sparsity of the data using dimensionality reduction by the SVD algorithm and hence recommend a list of movies based on the given input parameters
References
[1] F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=
[2] B.M. Sarwar, et, "Application of Dimensionality Reduction in Recommender System—A Case Study," Proc. KDD Workshop on Web Mining for e-Commerce: Challenges and Opportunities (WebKDD), ACM Press, 2000.
[4] Bobadilla, J., Ortega, F., Hernando, A., Gutierrez, A.: Recommender systems survey. Knowledge-Based Systems 46(0), 109–132 (2013)
[5]To view full code visit the following link:
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