Movie Recommendations

Recently, most retail-based and e-commerce companies have been using recommender systems aggressively. It retains a customer's interest by giving exclusive offers on personalized preferences. The primary purpose of a recommender system is to get at an increase in sales by providing an enriched experience to the customer. With the emergence of many video streaming services like Netflix, Hotstar, and amazon prime video, the dependency on movie recommendation systems has increased. It facilitates the users in faster search and easier access for shows matching their tastes and helps them choose what they are looking for without getting lost in the flood of available options. The user most often gets surprised by seeing an offer that they possibly would never have searched. The system is based on information retrieved and processed user preferences, ratings, likings, disliking, etc., to use this understanding to recommend the products. In this chapter, we have discussed the various popular algorithm used for the movie recommendation, along with an insight into the extensive use of models based on machine learning especially deep learning. The performance of different movie recommendation systems with a comparative analysis is also given to encourage further research in this area. 

Keywords: Collaborative filtering, Content-based filtering, Demographic filtering, Hybrid recommender, Personalization, Similarity test, Video ranker.