Optimized Item-based Collaborative Filtering Recommendation Algorithm
Although Item-based collaborative filtering recommendation algorithm is one of the most successful technologies in the recommendation systems,it still has such problem as poor recommendation quality.This paper presents an optimized Item-based collaborative filtering recommendation algorithm.In this paper,the calculation of similarity between items,the selection of neighbor items and prediction of ratings are optimized,which make the recommended result more meaningful and accurate.It can be proved that the optimized algorithm can solve the problem of the similarity measurement inaccuracy caused by the sparsity of data.The experiment results show that the algorithm is successful.