The Study Of University Library Collection Recommendation System Model Based On Data Mining

The application of data mining techniques in business area has brought great value, the value of which has been realized in many realms. The existing University Library management system has accumulated large information to provide recommend service.The paper systematically studies and analyzes some data mining algorithms, especially for meeting the fact need of the mining of University Library circulation and reducing calculation on irrelevant items, the paper focused on Apriori algorithm and improved its performance. Based on the improved Apriori algorithm, we designed a "personalized University Library collection recommendation system model". The system model uses categorization and cluster technology in data mining to find out groups of readers that have similar background and interest based on reader's identity, company and the kinds and numbers of books they borrowed.Moreover, the system model uses association rule technology in data mining to find out the most interested books in a group to help the librarians in book recommendation service. Through experiments, the results show that the designed mining scheme is reasonable to distinguish group first and then make recommendations based on reader's interests and the books recommended can normally meet readers'demands.Data mining in University Library of applications is still in its infancy, the ideas and practice of recommender system in this paper could be improved according to fact. The main purpose of this paper lies in how to apply data mining to University Library management system, and we hope that this paper could bring some benefits to scholars engaging in the similar research.