movie recommendation system








Abstract

Recommendation systems help users find and select items (e.g. books movies restaurants) from the huge number available on the web or in other electronic information sources. Given a large set of items and a description of the user & their needs they present to the user a small set of the items that are well suited to the description. Similarly a movie recommendation system provides a level of comfort and personalization that helps the user interact better with the system and watch movies that cater to his needs.In todays digital world where there is an endless variety of content to be consumed like books videos articles movies etc. finding the content of ones liking has become an irksome task. On the other hand digital content providers want to engage as many users on their service as possible for the maximum time. This is where recommender system comes into picture where the content providers recommend users the content according to the users liking. In this paper we have proposed a movie recommender system MovieMender. The objective of MovieMender is to provide accurate movie recommendations to users. Usually the basic recommender systems consider one of the following factors for generating recommendations; the preference of user (i.e content based filtering) or the preference of similar users (i.e collaborative filtering). To build a stable and accurate recommender system a hybrid of content based filtering as well as collaborative filtering will be used


Modules


Algorithms


Modification

machine learning




Price

₹12000 (INR)


Year

IEEE 2016