Keywords: React.js, Python, Flask, Recommendation system, JWT
This project implements a Movie Recommendation System that serves users and their movie recommendations. It is built with Python using Flask + React.js, and it utilizes collaborative filtering and correlation-based recommendation algorithms.
The core of the Movie Recommendation System lies in the application of collaborative filtering, a method of making automatic predictions (filtering) about the interests of a user by collecting preferences from many users (collaborating).
The underlying assumption of the collaborative filtering approach is that if a user A has the same opinion as a user B on a set of items, A is likely to have B's opinion for a given item that A has not rated yet.
In this application, the collaborative filtering method is complemented by the use of Spearman's rank correlation coefficient for quantifying the statistical relationships between users' movie ratings.
Collaborative filtering algorithms predict a user's interests by collecting preferences from many users. This technique assumes that if two users agree on one issue, they are likely to agree on others as well. In the context of this movie recommendation system, collaborative filtering is used to suggest movies that similar users have liked in the past.
In addition to collaborative filtering, this project uses a correlation-based recommendation system. Specifically, it employs the Spearman's rank correlation coefficient, a non-parametric measure of rank correlation. It assesses how well the relationship between two variables can be described using a monotonic function.
In the context of this system, it measures the strength and direction of the association between two users' rankings of movies they have both rated. The correlation coefficient ranges from -1 to 1. A value of 1 implies that a perfect increasing relationship exists between rankings, and a value of -1 implies a perfect decreasing relationship. This is used as a weight when predicting a user's rating for a movie they haven't seen yet, based on the ratings given by users who have a high correlation coefficient with them.