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http://dx.doi.org/10.14400/JDC.2021.19.5.175

Implementation of the Unborrowed Book Recommendation System for Public Libraries: Based on Daegu D Library  

Jin, Min-Ha (School of Management/Data Science, Handong Global University)
Jeong, Seung-Yeon (Department of Statistics, Kyungpook National University)
Cho, Eun-Ji (Department of Computer Science, Yeungnam University)
Lee, Myoung-Hun (Department of Mathematics, Kyungpook National University)
Kim, Keun-Wook (Big Data Center, Daegu Digital Industry Promotion Agency)
Publication Information
Journal of Digital Convergence / v.19, no.5, 2021 , pp. 175-186 More about this Journal
Abstract
The roles and functions of domestic public libraries are diversifying, but various problems have emerged due to internally biased book lending. In addition, due to the 4th Industrial Revolution, public libraries have introduced a book recommendation system focusing on popular books, but the variety of books that users can access is limited. Therefore, in this study, the public library unborrowed book recommendation system was implemented limiting its spatial scope to Duryu Library in Daegu City to enhance the satisfaction of public library users, by using the loan records data (213,093 cases), user information (35,561 people), etc. and utilizing methods like cluster analysis, topic modeling, content-based filtering recommendation algorithm, and conducted a survey on actual users' satisfaction to present the possibility and implications of the unborrowed book recommendation system. As a result of the analysis, the majority of users responded with high satisfaction, and was able to find the satisfaction was relatively high in the class classified by specific gender, age, occupation, and usual reading. Through the results of this study, it is expected that some problems such as biased book lending and reduced operational efficiency of public libraries can be improved, and limitations of the study was also presented.
Keywords
Public Library; Unborrowed book; Recommendation System; Content-based filtering; Convergence; Topic Modeling;
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