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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.

Project Features :-

1. User can register and login.
2. User can search through various movies and look through its details.
3. User can give rating to the movies.
4. User can add movie to their watch list.
5. User can get movie recommendation (Recommendation algorithm (Collaborative Filtering) which suggests new movies based on the ratings given by user.)

Algorithm :

Collabortive Filtering (Recommender Algorithm)

1. 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.

2. 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.

3. 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.

4. There are two types of collaborative filtering

   1. User-based, which measures the similarity between target users and other users.
   2. Item-based, which measures the similarity between the items that target users rate or interact with and other items.

   I have used user based collaborative filtering in this project.

Technologies Used

Web Technologies

Html , Css , JavaScript , Bootstrap , Django

Machine Learning Library In Python3

Numpy , Pandas , Scipy

Database

SQLite

Requirements

python 3.7

pip3

virtualenv

Installation

pip install -r requirements.txt --user

Run server locally

$ python manage.py runserver

Go to localhost:8000

1. Admin email - admin@admin.com
2. admin pass - admin

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