|Project Description: ||Recommendations that are personalized help the users in getting the list of items that are of their
interest in e-commerce sites. Majority of recommender systems use Collaborative Filtering
techniques to generate recommendations to their users. This project implements an information
filtering technique called as Collaborative Filtering for generating personalized
recommendations in movies for user. Collaborative Filtering is of two types, namely,
collaborative filtering based on users and collaborative filtering based on items. Collaborative
Filtering based on users is more expensive computationally but it produces better results.
Collaborative Filtering based on users is not preferred because it encounters the problems of
Scalability when the number of users increases. Therefore, we use item-based Collaborative
Filtering which is an alternative method. Collaborative Filtering, which is based on items uses
two techniques- Pearson correlation technique and Adjusted cosine technique for calculating the
similarity between items and to generate recommendations to users. In this Project both the
above techniques are used and to measure the accuracy of the predictions generated by these
techniques, Root Mean Square Error is computed.