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This paper proposes an effective model to realize the Webpage recommendation in personalized service based on the data mining technology. Applying user interest feature vector and user session feature vector to represents user’s interest and generate user groups’ interests by clustering, and extends the individual user interest. The experiment proves that the model is effective and accuracy. Introduction With the development of Internet, computer users are constantly submerged by huge information. Now people are looking for a service mode which can actively provide user interested information to the user and provide different service strategy and service contents to different users, that is, the personalized information service mode. Many researchers have proposed a variety of solutions to meet the individual needs of users. Information retrieval is the recommended technique based on content, it finds the qualified content in users’ given retrieval commands and conditions. Information filtering technique using the user model to describe user interest, it calculate the similarity of new Webpage and user model, and recommend the high similarity Webpage to the user. Ref. [1-3] proposes method to provide personalized service based on collaborative filtering, but they need user input additional information to determine the users personalized behavior. Personalized service based on the categories depends on the category setting and Webpage content classification. The rough classification leads to recall and precision of information is not high, and it is difficult to recommend accurately and interested Webpage to the user. Now people use data mining technology to mine the users which have similar browsing interest, the organizational structure of the web designers accordingly to adjust the page, so as to provide high quality service for the user. Ref. [4] provides three methods of real-time personalized recommendation, including two methods for clustering method and association rules. Ref. [5-7] takes different strategies to get the user's access pattern by mining users’ log, but they do not consider personalized recommendation. Personalized service based on previous browsing content is hard to capture the fresh Webpage and change of users’ interest. This paper combine the information retrieval, information filtering and data mining technology to construct an effective model to realize the Webpage recommendation in personalized service and to meet the user needs of checking the website. The Recommendation Model Structure This paper proposes user interest model which based on mining user's browsing behavior (recorded in the server logs), information retrieval and filtering technology, so the user's interest is represented by two features. The structure of model is shown in Fig.1. After getting the interest of individual user, we could found group of users’ interest by clustering, and the individual user interest can learn and extended from the group of user interest. In the process of user browsing, we will record, analysis and extract feature of the user access behavior and browsing Webpage, and transfer the user feedback to the individual user interests’ database, thus further supplement and International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015) © 2015. The authors Published by Atlantis Press 213 improve the individual user interests’ database. At the same time through the group clustering feedback to group user interests database, and then update the group user interests’ database. Fig.1. Recommendation Model Structure The Key Technology of User Interest Modeling The way of user interest modeling: (1) Building and updating user interest database according to the feedback information of search results through the user browsing Webpage. (2) In the case of without explicit engaging in of user, establish and update user interest through the observation of the user's browsing behavior. At present, mainly uses the Vector Space Model (VSM) to represent the documents. According to the attribute, document frequency and anti frequency of the term in the document, the document is represented as a feature vector of terms. If the term Ti appears in the document title and author, then its weight is 1. If the term appears in the main body of a document, we calculate the weight according to the term frequency and anti frequency. The weight Wi is as follows:

Webpage Recommendation Model in Personalized Service based on the Group Clustering