Collaborative filtering recommendation algorithm based onitem classification and cloud model
结合项目分类和云模型的协同过滤推荐算法

In order to solve problem of data sparseness in user rating matrix and the drawback of attributes' strictly matching in traditional similarity calculation method, this paper presented an improved collaborative filtering recommendation algorithm by combining the item classification and cloud model. This method firstly utilized the item classification information and cloud model to compute items inner-similarity, and then got the scores from neighbor items which had gotten the highest similarity and used their scores to forecast the unrated inner-class items. Secondly, this method obtained the user's neighbors through inner-class user similarity gained from the cloud model computing, then gave the final forecast grade and carried out the recommendation. Experimental results show that this algorithm is not only an effective solution to data sparseness and the drawbacks of traditional similarity method, but also improves the accuracy of user interest and nearest neighbor search. At the same time, the algorithm that only calculates the categories which adds the new users or items, it greatly increases the scalability of the system.