Recommeder_Systems

Three Recommenders have been made :

1. Simple Recommender (Popularity Based)
2. Content-Based Recommmender
3. Demograhic Recommender System

Link to dataset (228 MB):

https://www.kaggle.com/rounakbanik/the-movies-dataset/downloads/the-movies-dataset.zip/7

1. Simple Recommender (Popularity Based) :

   It offers generalized recommendations to every user based on movie popularity and (sometimes) genre. The basic idea behind this recommender is that movies that are more popular and more critically acclaimed will have a higher probability of being liked by the average audience. This model does not give personalized recommendations based on the user.

2. Content-Based Recommmender :

   These filtering methods are based on the description of an item and a profile of the user’s preferred choices. Here keywords are used to describe the items and a user profile is built to state the type of item this user likes. In other words, the algorithms try to recommend products which are similar to the ones that a user has liked in the past.

3. Demograhic Recommender System :

   This system aims to categorize the users based on attributes and make recommendations based on demographic classes. In Demographic-based recommender system the algorithms first need a proper market research in the specified region accompanied with a short survey to gather data for categorization. Demographic techniques form “people-to-people” correlations like collaborative ones, but use different data. The benefit of a demographic approach is that it does not require a history of user ratings like that in collaborative and content based recommender systems. It is not that complex and easy to implement.

Git clone the repo to your local system

git clone https://github.com/afrozchakure/Movie-Recommender-Systems.git