Recommender system framework based on datamining techniques

Product recommendation is a business activity that helps users to make the right decision and to decrease the time of waste and money. With increasing data amount, it is becoming popular day by day.There are two approaches to implement the recommender system: Collaborative Filtering and Content Based Filtering. Collaborative filtering thinks that what a user thinks in the past thinks same now and in the future. It tries to find close users. Content based filtering deals with searches and clicks of the user. It recommends similar items to those items.In order to demonstrate the efficiency of proposed model, a movie recommender application, CinreC, was developed. The model was constructed independently from the type of item. The system can be converted to other recommendation systems, such as books, music, TV programs, trips, news recommender systems by only changing the user interface. The experimental results show that proposed algorithm can efficiently perform online dynamic recommendation in a stable manner.