An item based collaborative filtering using item clustering prediction

Personalized recommender systems are producing recommendations and widely used in the electronic commerce. Collaborative filtering technique has been proved to be one of the most successful techniques in recommendation systems in recent years. However, most existing collaborative filtering based recommendation systems suffered from its shortage in scalability as their calculation complexity and space complexity increased quickly when the users and items in ratings database increases. To solve the scalability problem in the collaborative filtering, this paper proposed an item based collaborative filtering using the item clustering prediction. The methodology consists of five steps of clustering the items based on k means algorithm, predicting the vacant ratings where necessary, selecting the item clustering centers, forming neighbors from the selected item centers, and producing recommendations. The item based collaborative filtering utilizing the item clustering prediction is more scalable than the traditional collaborative filtering.