A Framework to Enhance the Movie Recommendation System by Using Data Mining

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@article{1702917,
      author = {G. Vani Tejaswi, S. Ravi Krishna, G. Leela Madhuri, CH. P. K. Prasanna},
      title = {A Framework to Enhance the Movie Recommendation System by Using Data Mining},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {3},
      pages = {47-60},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702917.pdf},
      abstract = {Recommendation System is a prime location which may be very famous and beneficial for humans to take right computerized decisions. It is a way that enables person to discover the statistics that is useful to him/her from sort of statistics available. When it involves Movie Recommendation System, advice is completed based  on diverse measures which can be used to discover similarity among customers for advice. In this paper, we have surveyed  ORBIT(Hybrid film suggest engine) ,Improved collaborative filtering set of rules, Knn(K nearest neighbour) collaborative filtering set of rules for film advice. We have additionally reviewed exceptional similarity measures. Various agencies like fb which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends merchandise etc. use advice device to boom their earnings and additionally gain their customers. This paper in particular concentrates at the short assessment of the exceptional strategies and its techniques for film advice, in order that studies in advice device may be explored.},
      keywords = {Recommendation System, CollaborativeFiltering, ORBIT, precision ,accuracy, similarity.},
      month = {September},
  }