Electronic commerce recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is the most successful technology for building recommendation systems. Unfortunately, the efficiency of this method declines linearly with the number of users and items. So, as the magnitudes of users and items grow rapidly, the result in the difficulty of the speed bottleneck of collaborative filtering systems. In order to raise service efficiency of the personalized systems, a collaborative filtering recommendation method based on clustering of users is presented. Users are clustered based on users ratings on items, then the nearest neighbors of target user can be found in the user clusters most similar to the target user. Based on the algorithm, the collaborative filtering algorithm should be divided into two stages, and it separates the procedure of recommendation into offline and online phases. In the offline phase, the basic users are clustered into centers; while in the online phase, the nearest neighbors of an active user are found according to the basic users’ cluster centers, and the recommendation to the active user is produced.
Collaborative Filtering Recommendation Algorithm based on Item Similarity Learning