Intelligent Movie Recommender System Using Machine Learning

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
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
movie.
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.
Intelligent Movie Recommender System Using Machine Learning