Movie Recommendation System using Collaborative Filtering

Based on user behaviour and preference patterns, a collaborative filtering movie recommendation system makes movie recommendations to users. The idea behind collaborative filtering is that users who have similar movie likes will probably appreciate similar content. When it comes to collaborative filtering, there are two primary methods:

User-Based Collaborative Filtering: This method identifies users with similar tastes (based on shared viewing preferences or ratings) and suggests films that these users have given high ratings. For instance, if User B views a film that User A hasn’t seen yet and User A and User B have given several of the same films comparable ratings, User A may be suggested that movie.

Item-Based Collaborative Filtering: Using user ratings as a guide, this technique searches for commonalities between the films themselves. A user may be suggested more films that have been rated similarly by others if they liked a specific film. For instance, Movie Y would be suggested to other users who appreciated Movie X if a large number of people who enjoyed Movie X also liked Movie Y.

In order to handle massive user-movie matrices by reducing dimensionality and collecting latent factors (hidden patterns) that influence user preferences, collaborative filtering usually uses matrix factorisation techniques like singular value decomposition (SVD). Because they can provide tailored recommendations based only on user interactions and without the need for comprehensive content information, these systems are particularly useful in streaming platforms.