Item-based And User-based Double Clustering Collaborative Filtering Recommendation Algorithm

With the popularity of the Internet and e-commerce application, consumers enjoy the convenience of shopping on the Internet; on the other hand, they have been in trouble of information overload. It is difficult for them to find their needed products within a mass of product information. Therefore, the recommendation system in e-commerce came into being. Recommended System in e-commerce platform plays the role of sales staff, recommends products to users to help users find the goods, which successfully completes the purchase process. Recommended System in e-commerce has good prospects for the development and application, gradually become an important research, which has been more and more attention.In this paper, we made a deep study of recommendation system in e-commerce, and then analyzed the status and prospects of the mainstream personalized recommendation technologies in e-commerce. Collaborative filtering technology is one of the main technologies for the recommendation system in e-commerce which has application of the earliest and is most successful. However, it has two problems: the lack of algorithm scalability and the sparsity of dataset.In order to solve these problems, we developed a hybrid recommendation system called Collaborative Filtering & User-Clustering and Item-Clustering based recommendation system. The Simulation results show that our method is more effective than traditional collaborative filtering algorithms.