The Research Of Data Mining In The Personalized Recommendation System Of College Library

With the rapid development of the information technology and Internet technology, the appearance and popular of lib 2.0 make the service spaces of the college library growing, personalized information service also gradually become the mainstream of new service model. Personal information service has changed the passive sevice mode of tradional library, recommending books to users depending on their intrest profile actively.The purpose of the topics is applying them to the personalized recommendation of college library by researching the theory of data mining technology and personalized personalization systems. This paper has a discovery with understand of Library integrated system, which used by majority of college librarys. The discovery is that these systems don't have data mining feature. In this paper, we will design a supporting system of personalized recommendation based on the library-integrated system. After having made the detailed demand analysis, the borrowing flow analysis, recommendation system model has been builded from angle of fundamental research,and Described each module of model.Due to limit of various conditions and time, this paper has a detailed research face to Data Acquisition Module and data mining module in the following research, has not a further discussion with recommendation module.In the study of concrete realization, this paper using data mining technology, making the log of borrowing and reading of one college library as the research object, studying concrete realization of data acquisition and mining analysis in the personalized recommendation system of college library. We mainly analysis for mining from two aspects, and carry on the personalization recommends according to the analysis result.On the one hand, we can mine the log of borrowing and reading Using association rule, so we can get the characteristics of the all kinds of readers. Analysing and generalizing different assembly of different readers borrowed. Actively provide what other readers of this kind borrowed, volunteer actively recommend books depending on the reader's interest profile in different forms.On the other hand, make Effective group classification of readers of library using clustering analysis. Summed up the characteristics of different groups, Recommend books for targeted, improve the rate of collection of books to borrowing.Make some suggestions about optimization clooections of library, and setting up scientific and reasonable structure of books of library collection. These recommendations to other University Libraries also have reference.Finally, I make a comprehensive summary for this research, and look to the future direction of further study.