Building Movie Recommendation System using Clustering, Collaborative Filtering, Matrix Factorization and improving the accuracy of the system by incorporating Opinion Mining

Abstract

Recommendation systems is a part of information filtering system which are gaining tremendous popularity and are one of the most used systems these days because of their ability to make suitable predictions depending upon the user’s requirements. A recommendation system is a system that predicts the rating, preference or reviews that a user would give to a particular item. They are used in a variety of fields including movies, music, books, news, and online shopping . When users face problems like information overload or when they are not able to describe what they exactly want, majority of time is wasted deciding what to watch. This is where recommendation systems come handy. They ease the task of deciding what to watch by predicting movies based on the user’s previously watched movies and ratings. They reduce time spent on deciding what to watch and provide the user a variety of options to choose from, according to his/her liking. Regrettably traditional recommendation system algorithms like collaborative filtering faces the problem of cold start and also may lead to substandard recommendation when user ratings of items are very sparse in comparison with the huge number of users and items .To overcome this problem in this research paper, we are trying to incorporate opinion mining which will help further to achieve better performance of recommender system.

Keywords: Collaborative Filtering (CF), Clustering, K-means Clustering, Matrix Factorization, Opinion Mining, Ratings and Reviews, Recommender System.

Cite this Article: Rudresh Zodge, Supriya Mohite, Jayma Jagadeeswaran , Batul Kachwala, Sourabh Landge. Building Movie Recommendation System using Clustering, Collaborative Filtering, Matrix Factorization and improving the accuracy of the system by incorporating Opinion Mining. International Journal Digital Communication and Analog Signals. 2020; 6(2): 1–5p.