A Modern Approach on Movie Recommendation Systems

In a rapidly changing world, recommendation systems have become a part of our daily lives, since it is used in many websites and has its applications in vast fields of movies, music, social media, books and also on e-commerce websites. Filtering & efficiently searching for the right things saves a lot of time.[4] Many people – including teenagers and adults – nowadays often use recommendation systems whether it is Movie Recommendation Systems or Music Recommendation systems or they may not even realize that they are using it embedded in any application or website that they use to watch movies or listen to songs or for any other purpose. Recommendation systems are significant because they help to choose the right items and services[3].  The present paper analyses the consequences of introducing personality into recommendation systems. To deliver more tailored movie suggestions, concepts like collaborative filtering are integrated with personality traits. Collaborative filtering & content-based filtering are two common approaches in recommendation systems[1,3]. The recommendation systems vary the formula for recommending products to the user. They have sentiments, age group, time spent in surfing something over the Internet/social media, likes/dislikes on surveys taken on various entertainment platforms (YouTube/Spotify for videos & songs respectively) as their attributes in the dataset. This study investigates the operation of numerous recommendation systems with the goal of combining two or more attributes to create a new recommendation system with enhanced efficiency & providing the best way to give recommendations to users.The concept that taking personality into consideration can increase recommendation quality is tested in this study. Our data suggest that personalisation improves recommendations, despite the fact that it necessitates some additional user input upfront. The film industry is no longer only industry or a source of entertainment; it has evolved into a global business hub. The box office success, popularity, & other aspects of a film are today celebrated all over the world. There is a wealth of information regarding the success and popularity of these films.Unlike current recommendation systems, which are based on a fixed set of attributes, this study offers the idea of combining two sets of attributes. However, these methods have several drawbacks, for example, the requirement of prior details of the user’s history and habits in order to fulfil the work of recommendation. To mitigate the impact of such dependencies, a hybrid recommendation system that includes collaborative filtering, content-based filtering, and sentiment analysis of movie tweets can be implemented. Where movie tweets are gathered from microblogging services in order to better understand current trends and user reactions to the film. Recommendation systems are a popular and valuable area for people to make informed automated judgments. It is a mechanism that allows a user to find information that is useful for an individual from a large amount of data.When it comes to the Movie Recommendation System, recommendations are made based on user similarities (Collaborative Filtering) or by taking into account a specific user’s activity (Content-Based Filtering). Various firms, such as Facebook-which promote friends, LinkedIn-which advises jobs, Pandora, Netflix, and Amazon are all employ recommendation systems to enhance their profits parallelly benefiting customers too[1,2]. The focus of the following study is on various tactics and methods which can be implemented so as to improve and enhance the quality of recommendation of movies.