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An Item Based Collaborative Filtering for Similar Movie Search

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • V. Arulalan

    (Madras Institute of Technology, Anna University, Department of Information Technology)

  • Dhananjay Kumar

    (Madras Institute of Technology, Anna University, Department of Information Technology)

  • V. Premanand

    (Madras Institute of Technology, Anna University, Department of Information Technology)

Abstract

A movie recommendation is vital in our social life because of its quality in giving improved excitement. Such a framework can recommend an arrangement of movies to users in light of their advantage, or the popularities of the movies. Despite the fact that, an arrangement of movie recommendation frameworks has been proposed, the vast majority of these either can’t prescribe a movie to the existing users productively or to another user by any methods. This paper proposes a movie recommendation framework that can extract from data and recommend similar movies to users, based on users input using Item based Collaborative Filtering. First, user item rating matrix is examined to identify relationships among various items, then finally similar movies were recommended based on user’s input. A part of this recommender system is execute using Apache Pig and Hadoop Distributed File System is used as data storage.

Suggested Citation

  • V. Arulalan & Dhananjay Kumar & V. Premanand, 2020. "An Item Based Collaborative Filtering for Similar Movie Search," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 949-955, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_96
    DOI: 10.1007/978-3-030-41862-5_96
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