Collaborative Filtering Recommendation Algorithm Based On Bidirectional Clustering Iterative Method

With the wide spread use of the Internet and the development of commerce on it, E-Commerce System becomes the most important platform to users who have commerce online. It provides more choice and convenience to users than before, but also brings some difficulties to users and the structure of E-Commerce System becomes more and more complex. Customers always confront with too many items and easy lost in it, they are difficult to find the products and services they wanted rapidly. Recommendation Systems emerge as the times require, it becomes the hotspot research field in E-Commerce, it will have good development and application perspective in the future.Recommendation System works based on the users'rating information or history records of users, it recommends the items or information to users which they are possible interested in. Many researchers apply some technologies of Data Mining to the research of Recommendation System, achieved great successful and developed some excellent Recommendation Systems, it greatly promote the development of recommendation technology. However, there are thousands of users and items in modern E-Commerce System, and with high increasing speed. So Recommendation System is confronting with some difficulties and challenges, such as: recommend accuracy, real-time requirement, data sparsity and scalable problem.In this dissertation, we propose two improved clustering-based collaborative filtering algorithms to solve the data sparsity problem. There are: Item Smoothing and Clustering Algorithm, Bidirectional Clustering Iterative Algorithm. By clustering users and items respectively, the nearest neighbors of target item can be found in the several most similar item clusters. In this way, we can reduce the search space, and improve the real-time performance of recommendation algorithm.In the Item Smoothing and Clustering Algorithm, based on the clusters'information we apply the smoothing strategy to the unseen rating data. The user-item matrix becomes densely, and then we find the nearest neighbors of target item in the item clusters. In this way we can solve the data sparsity problem in a certain extent.In the Bidirectional Clustering Iterative Algorithm, we use a bipartite graph to describe the associate relation between users and items. Using the crossing iterative algorithm to adjust initial user and item clusters to reach a steadily status. In this way, can also solve the data sparsity problem and improve the recommend accuracy, as the clusters become more accurate and more steadily.We conduct a series of experiments to examine the effectiveness of our new algorithms; these experiments are all based on the MovieLens dataset. First use K-mean algorithm to cluster users and items, observe the performance under different cluster number. Comparing the MAE (Mean Absolute Error) of the two new algorithms to several traditional collaborative filtering algorithms, such as: User-based, Item-based and Item-cluster collaborative filtering algorithm. The experimental results show that new algorithms could effectively alleviate the data sparsity problem and reduce the MAE, and improve the real-time performance of recommendation system.