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.
