Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media.
Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these
approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of
recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best
of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie
tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the
public database have yielded promising results.
