The Research Of Recommendation System In E-commerce Based On Web Data Mining

E-commerce is developing and being popularized globally due to its advantage of cheap, fast and not limited to space and time. Now its scale becomes wider and wider. E-commerce provides more and more choice to customer, but its structure is getting more and more complex at the same time. So there occurs a new problem. First the customers are not very interested with the commodities provided by the Web site and he may browse a lot of pages to find the commodity he wants. On the other hand, the website company can' t understand the whole needs about the customers so that it provides the customers most the same pages they don' t like and they both can' t maintain the steady relations between the company and the customer. The shortage of personalized service becomes the key factor which restricts the development of the E-commerce.Web data mining combines with the E-commerce just under the situation mentioned above. Web data mining is to find and retrieve interesting and potential useful model and hidden information. It combines the traditional data mining with the Web technology and it can be of great value in many aspects. Now Web data mining has become a new research direction of the data mining. The recommendation System in E-commerce based on Web data mining can meet the demands of future development of E-commerce.The main work of this thesis is listed as following:1. Analyze the actuality and development trend of the E-commerce and make an introduction about the Web data mining technology.2. Have a deep research about the recommendation algorithms used in the recommendation system of E-commerce and discuss the most popular collaborative filtering algorithm widely used in the recommendation of E-commerce.3. Based on the above research, I design a recommendation system of E-commerce using the technology of collaborative filtering algorithm based on clustering. And I improve the k-means clustering algorithm.4. Realize the clustering algorithm part of the recommendation system based on collaborative filtering and evaluate it.At the end of this thesis, I make a summary and conclusion about the finished work and discuss some further research directions in the future.