Research On The Behavior Of College Students' Library Based On Machine Learning

As university students learn to become more independent and independent,libraries play an increasingly important role as an indispensable part of learning resources.At present,the use of library resources by our school is still in its infancy,including: book inquiry,book borrowing,and book return.In today's data explosion and rapid information replacement,the various functions of the library in the primary stage can no longer satisfy the reader's pursuit of personalized recommendation service.Therefore,it is of great research value to provide a recommendation algorithm with depth and breadth for the library.At present,the library resource utilization efficiency can be improved from the following aspects: First,improve the retrieval precision of books or papers,so that readers can quickly and accurately find the required resources;second,according to the reader's borrowing records,It provides more personalized recommendations.This article will explore and research the intelligent digital library system from the perspective of strengthening personalized services,including the following research contents:Firstly,it summarizes the research on the recommendation function of digital library books by domestic scholars.Using CiteSpace software to visually analyze the journal articles in the field of book recommendation from 2013 to 2019,focusing on the centrality and mutation words,and drawing scientific knowledge maps based on keywords,authors and organizations,and research hotspots in this field.And the research frontier analyzes and provides theoretical basis for the later research.Secondly,aiming at the inaccuracy,inconsistency and non-genericity of the text and network data of educators and learners' books preferences in digital libraries,a recommendation system framework based on fuzzy ontology and genetic algorithm is proposed.The framework first introduces fuzzy logic into the domain ontology to process the fuzzy information in the book field,and then uses the genetic algorithm to weight the characteristics of the book.Finally,the recommended search space is narrowed down by the clustering algorithm to achieve refined recommendation results.The experimental results show that the proposed method can effectively improve the accuracy of book recommendation,and effectively solve the problems of cold start,data sparsity,uncertainty and subjective judgment in personalized service.Compared with traditional methods,accuracy and generalization.The ability has improved.