Massive-Scale Data Mining to Enhance Digital Library with Applications in College Education

Abstract

The context of digital library has changed from insufficient information to information overload, and its corresponding service mode should also change from "people looking for information" to "information looking for people." Using a grounded theory approach, this paper extracts 78 initial concepts, 24 basic categories, and 6 main categories by coding and analyzing the raw data obtained from the interviews. On this basis, the relationship path and action mechanism between categories are discovered; based on which a theoretical model of the influence mechanism of digital library intelligent information recommendation service satisfaction is constructed. The research results have shown that, under the moderating effect of user preference, the quality of data mining system, recommendation information quality, recommendation service quality, and recommendation form together have an impact on the satisfaction of digital library intelligent information recommendation service. The results of our work can provide useful reference for the optimization and healthy development of digital library services. Meanwhile, it has some theoretical and practical contributions. How to quickly obtain the information people required from a large amount of information is particularly important. Personalized construction is an inevitable service trend for the development of digital libraries in the new era. We in this paper study the current situation of digital libraries and the development of personalized services in digital libraries. We focus on data mining-related technologies, the development of digital library data exploration technologies, and the provision of Internet application services. The problems in this area were summarized, and the countermeasures were put forward based on this. It can be concluded that the concept of a digital library is not just a collection of data with information management tools, it is an environment that brings together collections, services, and people to support the entire data flow. It converts information into domain knowledge, from creation to dissemination. It guides the process from use to save. The trial registration number is ChiCTR2200055403.