Abstract
Information overload is a major problem for many internet users which occurs due to overwhelming amounts of data made available to a user. In order to deal with this problem filtering tool, like Recommender System is required for providing relevant information for the users which personalizes the search according to user preferences. The Collaborative Filtering Recommender System finds the nearest neighbour set of active user by using similarity measures on the rating matrix. This paper proposes different variations of K-nearest neighbors (KNN) algorithm with different similarity measures namely cosine, msd, pearson and pearson baseline for Movie Recommender System. These different variations of KNN algorithms have been implemented for real data from MovieLens dataset and compared on accuracy metrics like fraction of concordant Pairs, mean absolute error, mean squared error, root mean squared error, precision@k and recall@k for Movie Recommender System. For real life application, Movie Recommender System filtering tool may be used as plugin by customizing the web browser.
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Airen, S., Agrawal, J. Movie Recommender System Using K-Nearest Neighbors Variants. Natl. Acad. Sci. Lett. 45, 75–82 (2022). https://doi.org/10.1007/s40009-021-01051-0
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DOI: https://doi.org/10.1007/s40009-021-01051-0