Research On Application Of Collaborative Filtering In E-commerce Recommender Systems

With the development of network technologies, the application of E-commerce is being paid more and more attention. Especially, to an E-commerce Web station, as the marketplace where the enterprise does business with its customers, how can it make the products got more attraction to the customers and make more profits is a key problem. It is a good method that giving the customers personalized recommendation about the products. Collaborative filtering is a successful technology that is implemented in E-commerce recommender systems today. But, when the system scale (such as the number of customers or the types of products) is very large, collaborative filtering faces great challenges. In order to make the problems solved and improve the quality and efficient of recommender systems, some researches have been done, and the research results have been successfully applied in practice.The paper introduces E-commerce recommender systems and the typical technologies that are implemented in them, collaborative filtering technology and its two directions of existing algorithms. It gives emphasis to analyzing the problems which collaborative filtering is facing when it is applied in recommender systems and existing improved methods. Through comparing and analyzing between item-based collaborative filtering algorithm and user-based collaborative filtering algorithm, we proposed an improved method of item-based collaborative filtering algorithm. We theoretically detailed analyze the new method and prove its feasibility. Then the experimental results that the new method is implemented with the benchmark experimental data set are given, the performance between the new method and the old one is compared and analyzed. Finally, we summarize on the paper, point out defects and the directions that will be further studied in the future.