Movie swarm: Information mining technique for movie recommendation system

Huge amount of movies are available over the world, all of those are impossible to see for one user and all of them are not interesting. Movie recommendation systems filter out irrelevant movies and suggest the relevant movies those would be interesting for users. Traditional system can not recommend new users and new items efficiently. In collaborative filtering recommendation is based on users activities and products features hence when new users enter the system and new items added, it can not recommend. In content based recommend can recommend new items based on items features but unable to recommend new users. Therefor, We have proposed an information mining tool that collect all important information which is needed in movie recommendation system. In our proposed system, we have generated movie swarm which is very useful for movie producers and can solve new items problem. Also finds out which genres of movie should be recommended among followers, that solves new users recommendation problem. Experimental studies on the real data demonstrate the encourages and effectiveness of our methods.