Movie Recommendation Chatbot Using Cosine Similarity and NLP tools

Movie Recommendation Systems have been present since the inception of movie streaming platforms. These systems take the advantage of user’s data like watched movies, liked movies, location etc., to recommend similar movies using various algorithms. Recommendation Systems can be broadly classified into Content Based, Collaborative Filtering and Hybrid. All of these face the limitation of diversity, because of the similar data available user tends to fall in the loop of consuming similar type of content again and again. These systems do not take into account the current mood of the user, neither do they cater to multiple users using a single account. All of these limitations make the user experience dull. Our aim is to make a scalable chatbot that can cater to different needs of users without the drawback of previous data collected. Chatbot can directly recommend a diversified collection of movies by asking a few questions from users like their preferred genre, actors, directors and a few keywords. This helps with the diversity of recommendations with a ne w and interactive interface