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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.
Collaborative filtering filters information by using the interactions and data collected by the system from other users. It’s based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future.
When we want to find a new movie to watch we’ll often ask our friends for recommendations. Naturally, we have greater trust in the recommendations from friends who share tastes similar to our own.
Collaborative-filtering systems focus on the relationship between users and items. The similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.
There are two types of collaborative filtering
I have used user based collaborative filtering in this project.
Html , Css , JavaScript , Bootstrap , Django
Numpy , Pandas , Scipy
SQLite
python 3.7
pip3
virtualenv
pip install -r requirements.txt –user
$ python manage.py runserver
Go to localhost:8000
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