Research Of Search Engine Personalization Based On User Dictionary

Network is the bridge to obtain knowledges and send messages for people. However, in recent years,along with the high-speed development of the internet, the amount of information on the internet has increased tempestuously. For this reason, internet users often cannot find the needful information easily, a novel technology that can make full use of the information on the internet is on the tip of our tongue.Personalized Search is always the hot spot of subject of information retrieval in recent years, it makes complete the function deficiency in classifying users of the traditional search engine. This paper has contributed for following aspect:First, make use of the browsing history of the internet users reasonably and use a trategy based on the classical TF-IDF to establish a user dictionary before modeling. The adoption of UD has not only decrease the time complexity, but also give much support for double vector description.At the second place,this paper put forward a method to extract the text of the web document. For this will help capture the key interest of the users in order to optimize the user profiles. At the mean time,we use a way which contains information of clustering feedback to get rid of the frequently-emerged net-word.Finally, use an algorithm of terms-expansion to get some appropriate terms,which can be adopted to submit to search engine together wich the initial keyword. These terms can somehow represent the user's interest in information retrival. So by the use of them, the results of the search engine can be filtered to personalize the search engine and increase the efficiency.,This paper also attempt to research the main search engine as a part of the subject. This is because the commercial search engine is functionality and time efficient. We also developed an client component PSEplugin to implement the functions mentioned above. It is proved that the PSEplugin and the correlative technologies are effective and practical..