Recommender System Project with Heroku Deployment

Abstract

Nowadays, a recommendation system plays a very important role in various fields including e-commerce and OTT-platforms and has made things easier to find. A recommendation engine recommends the most relevant data to the user by using different algorithms. For watching favourable movies online we can utilise movie recommendation systems, which are more reliable, since searching for preferred movies will require more and more time which one cannot afford to waste. In this paper, to improve the quality of a movie recommendation system, a Hybrid approach by combining content based filtering and collaborative filtering, using SVM as a classifier and genetic algorithm is presented in the proposed methodology and comparative results have been shown which depicts that the proposed approach shows an improvement in the accuracy, quality and scalability of the movie recommendation system than the pure approaches in three different datasets. Hybrid approach helps to get the advantages from both the approaches as well as tries to eliminate the drawbacks of both methods.