Intelligent Movie Recommender System Using Machine Learning


Abstract. Recommender systems are a representation of user choices for the

purpose of suggesting items to view or purchase. The Intelligent movie recommender

system that is proposed combines the concept of Human-Computer

Interaction and Machine Learning. The proposed system is a subclass of

information filtering system that captures facial feature points as well as emotions

of a viewer and suggests them movies accordingly. It recommends movies

best suited for users as per their age and gender and also as per the genres they

prefer to watch. The recommended movie list is created by the cumulative effect

of ratings and reviews given by previous users. A neural network is trained to

detect genres of movies like horror, comedy based on the emotions of the user

watching the trailer. Thus, proposed system is intelligent as well as secure as a

user is verified by comparing his face at the time of login with one stored at the

time of registration. The system is implemented by a fully dynamic interface i.e.

a website that recommends movies to the user [22].

Conclusion and Future Work

The proposed Intelligent Movie Recommender System makes use of Semi-Supervised

Learning method for training data as well as sentiment analysis on reviews. The system

facilitates a web-based user interface i.e. a website that has a user database and has a

Learning model tailored to each user. This interface is dynamic and updates regularly.

Afterward, it tags a movie with genres to which they belong based on expressions of

users watching the trailer. The major problem arises with this technique is when the

viewer gives neutral face expressions while watching a movie. In this case the system is

unable to determine the genre of the movie accurately. The recommendations are

refined with the help of reviews and rating taken by the users who have watched that


A user is allowed to create a single account, and only he can log in from his account

as we verify face every time. The accuracy of the proposed recommendation system

can be improved by adding more analysis factor to user behavior. Location or mood of

the user, special occasions in the year like festivals can also be taken into consideration

to recommend movies. In further updates text summarization on reviews can be

implemented which summaries user comment into single line will comments. Review

Authenticity can be applied to the system to prevent fake and misguiding reviews. Only

genuine reviews would be considered for evaluation of movie rating. In future, the

system can be used with nearby cinema halls to book movie tickets online through our

website [22]. Our approach can be extended to various application domains to

recommend music, books, etc.