Improved algorithm of collaborative filtering based on item classification
基于项目分类的协同过滤改进算法*

To overcome the drawbacks caused by the data sparseness and inaccurate of the user neighbors,this paper came up with an improved collaborative filtering recommendation algorithm,basing on the technique of item classification.The algorithm first rated the unrated items by applying the item classification,and then calculated the user similarity within classes for nearest-neighbors,after which it could recommend the items based on the final prediction.Experimental results show that this algorithm can not only improve the accuracy of nearest neighbor search,but also increase the efficiency and scalability of the system.