Movie Recommendation System using Naive Bayes Algorithm with Collaborative Filtering

Anchal Dubey, Raju Ranjan

Abstract: In recent years, the movie industry is getting more and more prosperous. There are hundreds of movies released every year. However, it is difficult to notice the releasing of every movie, not to mention actually seeing it. Therefore, movie recommendation system has become more and more popular as a research topic. Until today so many numbers of recommendation algorithms have been proposed, where collaborative filtering and contentbased filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommendation systems recommend items by identifying other users with similar taste and use their opinions for recommendation whereas content-based recommendation systems recommend items based on the content information of the items. However, these systems suffer from scalability, data sparsity, overspecialization and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommendation system combines the individual system to avoid certain mentioned limitations of these systems. In this project, we propose a Movie Recommendation System by combining the Naive Bayes Algorithm with Collaborative filtering.

Keywords: Sentiment Analysis, Collaborative Filtering, Datasets, Android