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Research on Adaptive Teaching and Research Information Recommendation Model

Author: LuoWenBing
Tutor: WangMingWen
School: Jiangxi Normal University
Course: Computer Science and Technology
Keywords: Information Retrieval Adaptive User interest Vector space model
CLC: TP393.09
Type: Master's thesis
Year: 2010
Downloads: 70
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Abstract


With the rapid development of the Internet, the rapid growth of online information resources, people are faced with too much information can not be selected and digestion. On the Internet wide distribution of information resources and highly disordered great difficulties to find information of interest to the user. Due to the current search engines have a common nature, it is difficult to meet the user's query request of the different backgrounds and different purposes and different periods. The information needs of the users generally have individual features, these information needs are relatively stable, relatively long time, of course, will change over time changing the. Users look forward to the emergence of the information service software to adapt to the demand for personalized information. Personalized recommendation technology has become one of the focus of the current field of information retrieval. The so-called personalized service is recommended for different users to take a different service model to provide different search content, the key is to know the user's interest, and accurately establish the user interest model. The user interest model accurately reflects the user's interest determines the quality of the system to provide personalized recommendation services. This paper presents a personalized recommendation model based search result clustering analysis, first of all the current major web mining technology and user interest modeling techniques discussed analysis of the information content of the teaching and research to the user's browser-based and user browsing behavior analysis, supplemented by the user interest model. Then, the document representation techniques, including the text of the vector space model representation, characterized in item selection, the text representation of the structure of the vector space model. The contents of the document and the comparison of user interest model commonly used similarity function can be used to calculate the similarity. Recommended adaptive re-use a variety of information fusion algorithm to re-sort the search results, the results of cluster analysis, adjust the different types of document weights. Finally, the improved method to do a more comprehensive experiment. Analysis according to the teaching and research information push experimental results show that the proposed user interest model more accurately describes where the user is interested in personalized push services has practical application value. Personalized recommendation based on the results of clustering analysis model can better improve recommended effect. The innovation of this paper is to record the user's behavior transformed into the feedback of user interest model, the adaptive recommended for users of teaching and research information.

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