University Library Lending System Model Based on Fractional Differential Equations

Abstract

How to find books suitable for them from the massive book information is a problem that needs to be considered at present for university library users. This paper proposes a personalized recommendation system for digital libraries utilizing fractional differential equations. At the same time, we use the idea of a collaborative filtering algorithm to recommend books for new users. Finally, we use the accurate data of the library to design a personalized book recommendation system for university libraries. The research shows that the university library lending system based on fractional differential equations has improved user experience.