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Personalized Research Combining with Analysis of Web User’s Behavior Based on the User’s Browsing Content

Author: PanYanJun
Tutor: ZhangGang
School: Tianjin University
Course: Applied Computer Technology
Keywords: Text Clustering User interest model Data Mining Web Mining Vector space model Personalized service
CLC: TP393.092
Type: Master's thesis
Year: 2005
Downloads: 360
Quote: 3
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Abstract


Internet and WWW rapid development at an alarming rate , making the Web site design and maintenance work has become particularly important. Researchers placed in front of the new issue is how to manage large amounts of information on the WWW , in order to meet growing user demand for personalized information . Personalized service information technology has become one of the hot areas of research services . The so-called personalized service refers to different users to take a different service strategy, providing different services, the key is to know the user's interest , and accurately establish user interest model . Firstly, data mining techniques are described, and then analyzes the current major Web mining techniques and user interest modeling techniques proposed to browse the contents of the Web user browsing behavior analysis -based and supplemented by user interest mining process model . Then , a preliminary study and discussion pages of text representation techniques , including: text vector space model that feature item selection and extraction algorithm , the text of the page is represented as a structured vector space model format . Then , the paper focuses on the clustering analysis of the text pages and user interest model of two aspects . By calculating the similarity between the text , the text set by cluster analysis . After comparing the existing clustering algorithms and practical application environment, proposes a hierarchical clustering method (agglomerative algorithm) and flat- division method (K-means algorithm) combined with the new algorithm . In the clustering results , based on a two-story tree- user interest model weighted vector format to represent each user's interest . In order to facilitate the use of user interest model and updates every interest in using the vector space model class also expressed interest in the class content page and can be compared to the commonly used similarity function used to calculate the similarity . Finally, a simulation test , the theory of visualization , and concrete. Through trial also demonstrated in this paper improved clustering algorithm is simple , high accuracy ; proposed user interest model can accurately describe the user's interest lies in personalized recommendation service that has practical value.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > The application of computer network > Web browser
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