Joining User Clustering and Item Based Collaborative Filtering in Personalized Recommendation Services

Personalized recommender systems consist services that produce recommendations and are widely used in the electronic commerce. Many recommendation systems employ the collaborative filtering technology. With the gradual increase of customers and products in electronic commerce systems, the time consuming nearest neighbor collaborative filtering search of the target customer in the total customer space resulted in the failure of ensuring the real time requirement of recommender system. To solve the scalability problem in the collaborative filtering, this paper proposed a personalized recommendation approach joins the user clustering technology and item based collaborative filtering. Users are clustered based on userspsila ratings on items, and each cluster has a cluster center. Based on the similarity between target user and cluster centers, the nearest neighbors of target user can be found and pre-produce the prediction where necessary. Then, the proposed approach utilizes the item based collaborative filtering to produce the recommendations. The recommendation joining user clustering and item based collaborative filtering is more scalable than the traditional one.