The Design And Realization Of Personalized Recommendation System Of The Library Business Data Analysis Services

In recent years, science and information technology has a very rapid development, in this background, the construction of campus informatization has made great achievement in our country’s colleges and universities, and plays a pivotal role in the development and reform process of our colleges and universities. the emergence of major teaching system, such as student information management system, online courses system, student course management system, campus card system and digital library, etc, makes up for the weakness of the university hardware resources and teaching resources to a great extent. Attendant problem is the growing number of electronic literature resources of the university library, the university internal information system presents information island state, lacks of connectivity and linkage, through the integration of the internal information, eliminating the information island, to provide comprehensive and personalized service at home and abroad has become the development trend of the construction of digital campus.This paper uses the data of students’ borrowing, collection, evaluation and social relationship in the library as the foundation, excavate the students’ personalized interest information deeply, and then design and realize the students’ individualized information recommendation service. In students’ individualized information recommendation, recommend system mainly design and finished user items recommend module, user friend recommended module, user tag recommendation module items recommend module based on the label.The main function of the user items recommended module is to recommend users interested resources by Item-based collaborative filtering and user-based collaborative filtering, user’s friends recommended modules recommend interested friends for users according to the user’s attributes, interests, friends relations, ser tag recommendation module contains the user recommend tags and give users interested recommended resources based on the user’s label.