A movie recommendation is important due to today's pop culture, as it provides enhanced entertainment. Such a system can suggest a set of movies to users based on their interest, or the popularities of the movies. Some popular systems would include Netflix and Hulu. There has been an increasing demand of Recommendation Systems for movies these days, as people don't want any hassle in picking movies of that would interest them. In today's context, many forms of recommendation systems are implemented such as content based, collaborative filtering, popularity based recommendation engines, hybrid approach, etc. We have developed a full-stack web application which uses a live movie source, TMDb, which maintains all real-world movies in their database, with their genres, date released, IMdb scores, etc . The users will have the ability to register themselves in our system so that we can track their browsing activity in our website. The website uses an effective movie filter in the discover section which will solve the problem of filtering movies and we use The idea behind Content-based (cognitive filtering) recommendation system, which is to recommend an item based on a comparison between the content of the movies such as their genres, IMDb score, etc and a user profile, to generate a list of movies tailored specifically for a particular user.
- TMDb APIs
- MongoDB
- Express
- Angular
- Bootstrap & Font Awesome
- Clustering
Dependencies listed in package.json
npm install // After installing node and angular
mongod.exe
node index.js
ng serve
- Aditya Verma
- Vishruth L
- Vishnu Charan