Research On User Interest Model In Personalized Search Engine

Network capacity growing and information update accelerating, redundant information, mixed themes occur one after another, and people find it difficult to retrieve the information they want efficiently. In the process of solving these problems, search engine providing personalized services which enhances retrieval efficiency come into focus. The search engine provides information which users are interested in initiatively, and offers different service strategies and content for different users, i.e. personalized service mode of information.In this paper, not only modeling technology of personalized search has been studied, but also sprint algorithm and agent technology together have been applied to the modeling process, which improve the speed and accuracy of modeling. Thus, interest model is closer to the user's actual preferences. The model analyzes the user's performance, forecasts their interest, and optimizes their query statements, so it finally can raise the efficiency of information retrieval.This paper provide the process interest model. First, data structure design. According to the implementation requirement of sprint, three tables of special structure have been designed which store required data in different modeling stages. Then, sprint algorithm application. The algorithm applied in data modeling is an innovative point of this article. The establishment of interest model as well as the implementation of data extraction leaning in each step is the main focus of this paper. Finally, interest model establishment. For the interest model, this paper proposes a unique establishment method and a model structure. Data mining algorithm is fully applied in the modeling process, and interest model is extracted while building interest tree with the user's link operation.The essence of this thesis is to establish an interest model which is accurate and close to the user's requirement. This interest model provides different user's orientations with different personalized services. When users lose in messy network resources, the model can help them find what they want effectively and efficiently.