Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
This project is about the Movie Recommendation developed with the help of coorelation.
Recommender Systems They are simple algorithms which aim to provide the most relevant and accurate items to the user by filtering useful stuff from of a huge pool of information base by learning consumers choices and producing outcomes that co-relates to their needs and interests.
Types of Recommendation Systems :
Content-based systems 2. Collaborative filtering systems
This program is based on Item-based Colloborative filtering.
We have used the MovieLens dataset for this purpose. It has been collected by the GroupLens Research Project at the University of Minnesota. MovieLens 100K dataset can be downloaded from http://grouplens.org/datasets/movielens/100k/. It consists of:
- 100,000 ratings (1-5) from 943 users on 1682 movies.
- Each user has rated at least 20 movies.
- Simple demographic info for the users (age, gender, occupation, zip)
- Genre information of movies