Abstract:  The user’s interest model is the basic and core component in a personalized services system, but user’s interest will change over time, interest drifting problem cause the decrease of the prediction performance.
Therefore, this paper presents a user interest model and the approach for dealing with the interest drifting problem based on a hierarchical vector space model，term form interest topic，user profile is formed with interest，then
hierarchical vector space model ( VSM) set up based on the user profile. Calculate the degree of user interest in the Web page based on the set of user browsing behaviors, to estimate user interest of Web pages quickly, in order to
improve the practicality of personalized systems, which meet the personalized needs of users.The presentation of user model determines its ability to express user’s real information and ability to calculate, but also it limit choice of
the method for user modeling to some extent, In this paper, we view the text features as computable form of the instance and subject’s characteristics，using term frequency-inverse document frequency(TF-lDF) formulation to
calculate term weight，using cosine to calculate feature similarity. Although feature selection based on entropy can obtain better results, with high cost of computing and space consumption, which is not conductive to the online
personalized recommendation system for practical application. As user profile need to update with the change of user’s interests, the user model updation algorithm in connection with established model is proposed，which
consider the updation of subject’s characteristics and weight fully, using user feedback within a fixed window to update incrementally, with lark overhead，and thus selecting the relevant param eters can improve the adaptability
of the method. Experiment results show that the design of the user model and the update mechanism that can effectively improve the prediction performance for personalized services with improvement on accuracy and recall.