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引用本文:????,?С??,?С??,?????.??У???????????????????齨???????[J].图书情报工作,1957,64(3):59-70.
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Prediction of Reader Lending Trend in Academic Library by Linear Regression Modeling
Wang Hong,Yuan Xiaoshu,Yuan Xiaoling,Huang Jianguo.Prediction of Reader Lending Trend in Academic Library by Linear Regression Modeling[J].Library and Information Service,1957,64(3):59-70.
Authors:Wang Hong  Yuan Xiaoshu  Yuan Xiaoling  Huang Jianguo
Institution:1.Library, Shanxi University of Finance and Economics, Taiyuan 030006;2.School of Information, Shanxi University of Finance and Economics, Taiyuan 030006;3.Library, Taiyuan Science and Technology University, Taiyuan 030024;4.Taiyuan Daran Science and Technology Co. Ltd, Taiyuan 030006
Abstract:??Purpose/significance] By means of the classification and circulation data of library collection, the paper finds the close correlation between reader characteristics and library collection circulation, establish the relationship model. And through model fitting and prediction, this study explores the implicit rule between reader and library circulation which provides technical and means support for the intelligent management of library.Method/process] Firstly, this paper used clustering and correlation analysis techniques to extract the macroscopic observable characteristics of readers, constructed the direct and indirect mapping relationship between reader characteristics and book classification, and then constructed the regression model of the circulation of reader characteristics and classified books, and verified the validity of the model and optimized the goodness of fit of the model. According to the effective model, this paper explored the trend change of library circulation, and sum up the underlying rules of knowledge construction of the macroscopic characteristics of readers, as well as the impact on the circulation of books.Result/conclusion] There are 3 classification characteristics of readers, namely, the professional learning direction representing the social role requirements of readers, the enrollment batch representing the interaction effect between readers and the number of readers, which can effectively fit and predict the book circulation. The prediction results show that the model has high accuracy and can be used as an effective tool to provide reliable technical support for library to develop knowledge service.
Keywords:university libraries  circulation prediction  data mining  linear regression  
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