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Improved Cluster-Minkowski Recommendation System (ICRS) for Collaborative Filtering

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Recommendation systems are intelligent system which provides suggestion according to user adaptability. Recommender systems i.e., collaborative filtering and content filtering works on the basis of user profiles, extensive history of user preferences and item descriptions. This paper proposes an improved recommendation system based on clustering approach. The comparative analysis shows that the proposed system provides better results in terms of RMSE as compared to other already existing methods.
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Keywords: Clustering; Collaborative Filtering (CF); Content Filtering; Improved K-Mean; K-Mean; Matrix Factorization; Stochastic Gradient Descent (SGD)

Document Type: Research Article

Affiliations: Computer Science and Engineering Department, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala 133207, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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