Item-clusteringRecommendation Algorithm Based on Artificial Bee Colony

Aiming at improving the accuracy and real-time problem exposing in collaborative filtering recommendation algorithm, A new Item-clustering recommendation algorithm based on artificial bee colony algorithm is proposed. In the process of data processing, the artificial bee colony clustering algorithm is used to cluster the items, Search for the nearest neighbors of the target item from the highest similarity of several clusterings, in order to eliminate the interference of similar items to improve the accuracy of the recommendation, At the same time, it also greatly reduces the item space and improves the real-time recommendation; In the process of scoring, the time weighting function is used to optimize the traditional score prediction model in order to improve the accuracy of the prediction, so as to improve the accuracy of recommendation. The experimental results show that the accuracy and real-time performance are improved effectively.