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Movie Recommendation System Project Using Collaborative Filtering, Python Django, Machine Learning

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Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.

A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.

A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.

Algorithms Implemented 

  • Content based filtering
  • Collaborative Filtering
    • Memory based collaborative filtering
      • User-Item Filtering
      • Item-Item Filtering
    • Model based collaborative filtering
      • Single Value Decomposition(SVD)
      • SVD++
  • Hybrid Model
    • Content Based + SVD

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy



python 3.6



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