Study On E-Commerce Recommendation System Based On Web Mining

With the popularization of Internet and the development of E-Commerce, E-Commerce system at the time of providing more and more choices for customers, its structure also becomes more and more complex. And the users feel confused facing so many items to choice, because it is hard for them to find out what they really need and where what they need are. The E-Commerce recommendation systems can interact with the users, recommend items to them and help them to find out where the items they really want. So under the recommendation systems' help, the buying procedure is very smooth. Facing the tough competition, the E-Commerce recommendation system can help enterprises to hold the customers efficiently, boost the sale volume and enhance the competition power.The recommendation system has an amazing development prospect. It is becoming a hot issue among the IT technology, and it attracts more and more attention of researchers.E-Commerce recommendation system has made a great advance both in theory and in practice. But it also faces a series of challenges when its scale becomes bigger. Aiming at these challenges, the thesis studies E-Commerce recommendation system from three aspects:Firstly, the thesis presents a framework of recommendation system based on web mining. Traditional collaborative filtering recommendation technique is hard to provide recommendation service for unregistered users. To overcome this problem, the author suggests a framework of recommendation system based on web mining. This method first clusters web usage data, web content data and web structure data respectively, then provides high-quality recommendation services based on mining results. Compared with traditional collaborative filtering techniques, recommendation systems based on web mining are convenient for users, because they needn't to provide subjective rating information.Secondly, the thesis presents a new recommendation algorithm for the new...