Digital Footprint Analytics for User Behavior Prediction in Libraries

Abstract

Abstract - As libraries seek to have more people satisfied as well as to manage resources better, the application of data analytics in the digital age sounds almost ubiquitous. This paper will look at the use of digital footprints to forecast behavior in libraries. This paper will build models on the behavioral pattern of users, such as logins, use of resources, search, and borrowing history, so that it can serve users better and also to maximize resource allocation. This model employs machine learning techniques, namely, supervised models, such as logistic regression and decision trees to extract patterns and relationships in user data. Incorporating digital footprint analysis in libraries enhances personalized service and helps librarians make sound administrative judgments. Libraries are used to collect data, which will be anonymized to ensure that the identities of users are not revealed and to conform to ethics. The suggested structure would enable digital libraries to more efficiently predict user actions and proactively propose the appropriate content. This case demonstrates the significance of behavioral analysis in building responsive, self-learning, and user-optimized intelligent library systems. The findings contribute to the rationale of the increasing literature on digital libraries and also highlight the development of library analytics in providing library services to academic and public libraries.