A Library Book Recommendation System Based On Data Minging Technology

Now in the university library,the number of books is not a problem.Even an ordinary higher vocational college library,the volume of books can reach hundreds of thousands or even millions of copies.However,it is a very worthwhile study that how to let readers know what books they like,what books worth reading.This paper first introduces and compares library recommendation system in china and abroad,and introduces several common recommendation methods,including the three recommended methods that used in this system.The first is statistical analysis techniques.Through statistical analysis technology,we can summarize and analyze the lending situation of the readers in a targeted way and make the readers have a clear understanding of their lending situation.At the same time,we can obtain the most popular books,new books and other recommended information.The second is collaborative filtering technology.For real-time recommended consideration,the system does not use the traditional collaborative filtering method to calculate and maintain a reader rating matrix.Rather,it simplifies the calculation of reader similarity by comparing the number of readers borrowing the same book to determine the similarity of readers to get similar readers.And in the final recommendation,through the chinese library classification to improve the accuracy of the recommendation.The third is association analysis technology.Use SPSS analysis software,through the association of circulation data analysis,find the relationship between books,the related books can be recommended.Using the above recommended method,get the relevant recommendation information,I realized the web-based library books recommendation system.The system uses(B / S)mode,use Think PHP framework to developed.System function module is divided into user module,visitor module and password update module.Readers browse any page of the system,will get the appropriate targeted book recommendations.Among them,the main recommendations are displayed in the personal home page and book details page.Through the testing and comparison of the recommended system,it shows that it did a good job of our initial idea.The classification of professional books for students is not enough,which will continue to improve in the follow-up work.