An Interactive and Inclusive Movie Recommendation Model Based on User Preferences

Now a day's recommendation system has changed the style of searching the things of our interest.This is information filtering approach that is used to predict the preference of that user.The most popular areas where recommender system is applied are books, news, articles, music, videos, movies etc.In this paper we have proposed a movie recommendation system.It is based on content filtering approach that makes use of the information provided by users, analyzes them and then recommends the movies that is best suited to the user at that time.The recommended movie list is sorted according to the ratings given to these movies by previous users and it uses K-means algorithm for this purpose.It also help users to find the movies of their choices based on the movie experience of other users in efficient and effective manner without wasting much time in useless browsing.This system has been developed in PHP using Dreamweaver 6.0 and Apache Server 2.0.The presented recommender system generates recommendations using various types of knowledge and data about users, the available items, and previous transactions stored in customized databases.The user can then browse the recommendations easily and find a movie of their choice.