Research Article
College Student Reader Model and Its Reading Recommendation Algorithm Based on Data Mining
@INPROCEEDINGS{10.1007/978-3-031-63130-6_17, author={Xin Peng and Lixin Nie}, title={College Student Reader Model and Its Reading Recommendation Algorithm Based on Data Mining}, proceedings={Application of Big Data, Blockchain, and Internet of Things for Education Informatization. Third EAI International Conference, BigIoT-EDU 2023, August 29-31, 2023, Liuzhou, China, Proceedings, Part I}, proceedings_a={BIGIOT-EDU}, year={2024}, month={7}, keywords={Data mining College students Readers Model Reading recommendation Algorithm research}, doi={10.1007/978-3-031-63130-6_17} }- Xin Peng
Lixin Nie
Year: 2024
College Student Reader Model and Its Reading Recommendation Algorithm Based on Data Mining
BIGIOT-EDU
Springer
DOI: 10.1007/978-3-031-63130-6_17
Abstract
This paper expounds the necessity of applying data mining technology to the recommendation service of university library, and introduces the present situation of applying data mining technology to the literature resource recommendation service, literature resource retrieval service and literature resource management service of library. The informatization construction of colleges and universities is one of the important fields of social informatization construction in China, and it is an important measure to comprehensively improve teaching quality and scientific research ability. The reading behavior of university readers is biased, utilitarian, the reading content tends to popular culture, the classical reading consciousness is relatively weak, and the reading style is digital. University library is an indispensable part of cultivating high-quality talents, and its informatization construction degree affects the cultivation level of college students’ overall quality to a certain extent. As a service institution of higher education, university library should fully understand the reading trend of readers and grasp their reading interests, which directly affects the service quality of university library. The existing university library management system has accumulated a large amount of reader information and borrowing history information, which provides a data basis for recommendation service. This paper analyzes the three influencing factors of the construction of precision recommendation service model based on College Students’ reading data mining technology, namely database construction, precision recommendation calculation method and reader oriented operation process.
