Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

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

공공도서관 미대출 도서 추천시스템 구현 : 대구 D도서관을 중심으로

  • 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)
  • 진민하 (한동대학교 경영학/데이터사이언스학과) ;
  • 정승연 (경북대학교 통계학과) ;
  • 조은지 (영남대학교 컴퓨터공학과) ;
  • 이명훈 (경북대학교 수학과) ;
  • 김건욱 (대구디지털산업진흥원 빅데이터활용센터)
  • Received : 2021.02.03
  • Accepted : 2021.05.20
  • Published : 2021.05.28
Download PDF
⟨ Previous Next ⟩

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.

국내 공공도서관의 역할과 기능은 다양해지고 있는 반면, 내부적으로는 편향된 도서 대출로 다양한 문제들이 나타나고 있다. 또한 최근 4차 산업혁명으로 공공도서관에서 인기도서 위주의 도서 추천시스템이 도입되고 있으나, 이용자가 접할 수 있는 도서의 다양성은 제한되고 있다. 이에 본 연구에서는 공공도서관 이용자의 만족을 제고하기 위해 공간적으로는 대구시 두류도서관으로 한정하여 대출이력 자료(213,093건), 회원정보(35,561명) 등을 활용하여 군집분석과 토픽 모델링, 콘텐츠 기반 필터링 추천 알고리즘으로 공공도서관 미대출 도서 추천시스템을 구현하였으며, 이에 대한 실제 이용자들의 만족도 설문조사를 실시하여 미대출 도서 추천시스템의 가능성과 시사점을 제시하였다. 분석 결과 대다수의 이용자들이 높은 만족도로 응답하였으며, 특정 성·연령대, 직업, 평소 독서량 등으로 분류된 계층에서 만족도가 상대적으로 높게 나타난 것을 확인할 수 있었다. 본 연구결과를 통해 공공도서관의 편향된 도서 대출, 운영 효율성 저하 등의 문제를 일부 개선할 수 있을 것으로 기대하며, 연구의 한계점 또한 제시하였다.

Keywords

References

  1. Y. S. Kim. (2007). A Study on the Effective Enhancement of the Public Library Infrastructure in Korea: Focusing on the Operation of Small Public Libraries in the UK. Journal of Korean Library and Information Science Society, 38(1), 29-48. DOI : 10.16981/kliss.38.1.200703.29
  2. Y. J. Shin & Y. H. Noh. (2020). A Study on the Program Structure and Activation Plan of the Complex Cultural Space in Public Library. Journal of the Korean Society for Library and Information Science, 54(1), 89-114. DOI : 10.4275/KSLIS.2020.54.1.089
  3. W. S. Kim. (2019). Content Recommendation Techniques for Personalized Software Education. Journal of Digital Convergence, 17(8), 95-104. DOI : 10.14400/JDC.2019.17.8.095
  4. H. Y. Cho. (2017). A Experimental Study on the Development of a Book Recommendation System Using Automatic Classification, Based on the Personality Type. Journal of Korean Library and Information Science Society, 48(2), 215-236. DOI : 10.16981/kliss.48.201706.215
  5. Y. J. Jung & Y. H. Cho. (2017). Topic Modeling-based Book Recommendations Considering Online Purchase Behavior. Knowledge Management Research, 18(4), 97-118. DOI : 10.15813/kmr.2017.18.4.004
  6. Y. S. Jeong. (2018). A study on the Book Recommendation Model Using Big Data.
  7. I. S. Choi & S. K. Park & Y. H. Cho & C. Y. Lee & A. W. Kim & W. C. Kim. (2020). Developing a Book Recommendation Model Based on Reading Level Using Book Information and Book Review Data. Journal of the Korean Institute of Industrial Engineers, 46(3), 179-189. DOI : 10.7232/JKIIE.2020.46.3.179
  8. D. W. Park. (2020). A Study on Architecture for BigData-based Book Curation System
  9. Keita Tsuji et al. (2014). Book Recommendation Based on Library Loan Records and Bibliographic Information. Journal of Procedia - Social and Behavioral Sciences, 147, 478-486. DOI : 10.1016/j.sbspro.2014.07.142
  10. Yonghong Tian & Bing Zheng & Yanfang Wang & Yue Zhang & Qi Wu. (2019). College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm. Journal of Procedia CIRP, 83, 490-494. DOI : 10.1016/j.procir.2019.04.126
  11. B. H. Oh & J. H. Yang & H. J. Lee. (2014). A Hybrid Recommender System based on Collaborative Filtering with Selective Utilization of Content-based Predicted Ratings. Journal of KISS : Software and Applications, 41(4), 289-294. DOI : 10.1016/j.procir.2019.04.126
  12. K. J. Kim & H. C. Ahn. (2009). User-Item Matrix Reduction Technique for Personalized Recommender Systems. Journal of Information Technology Applications & Management, 16(1), 97-113.
  13. Y. J. Lee & C. H. Lee. (2020). What Do The Algorithms of The Online Video Platform Recommend: Focusing on Youtube K-pop Music Video. Journal of the Korea Contents Association, 20(4), 1-13. DOI : 10.5392/JKCA.2020.20.04.001
  14. Airoldi. M & Beraldo, D & Gandini, A. (2016). Follow the algorithm : An exploratory investigation of music on Youtube. Journal of Poetics, 57, 1-13. DOI : 10.1016/j.poetic.2016.05.001
  15. S. Y. Yun & S. D. Yoon. (2020). Item-Based Collaborative Filtering Recommendation Technique Using Product Review Sentiment Analysis. Journal of the Korea Institute of Information and Communication Engineering, 24(8), 970-977. DOI : 10.6109/jkiice.2020.24.8.970
  16. J. Y. Kang & H. S. Lim. (2019). Proposal of Content Recommend System on Insurance Company Web Site Using Collaborative Filtering. Journal of Digital Convergence, 17(11), 201-206. DOI : 10.14400/JDC.2019.17.11.201
  17. H. W. An & N. M. Moon. (2019). Influential Factor Based Hybrid Recommendation System with Deep Neural Network-Based Data Supplement. Journal of Broadcast Engineering, 24(3), 515-526. DOI : 10.5909/JBE.2019.24.3.515
  18. E. Y. Bae & S. J. Yu. (2018). Keyword-based Recommender System Dataset Construction and Analysis. Journal of Korean Institute of Information Technology, 16(6), 91-99. DOI : 10.14801/jkiit.2018.16.6.91
  19. J. E. Son & S. B. Kim & H. J. Kim & S. Z. Cho. (2015). Review and Analysis of Recommender Systems. Journal of the Korean Institute of Industrial Engineers, 41(2), 185-208. DOI : 10.7232/JKIIE.2015.41.2.185
  20. Barragans-Martinez, A. B. & Costa-Montenegro, E. & Burguillo, J. C. & Rey-Lopez, M. & Mikic-Fonte, F. A. & Peleteiro, A. (2010). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information sciences, 180(22), 4290-4311. DOI : 10.1016/j.ins.2010.07.024
  21. Y. J. Jung & H. J. Kim. (2020). A Study on the School Library Research Trends Using Topic Modeling. Journal of Korean Library and Information Science Society, 51(3), 103-121. DOI : 10.16981/kliss.51.3.202009.103
  22. H. S. Kim & B. R. Kang. (2019). Analysis of Research Topics among Library, Archives and Museums using Topic Modeling. Journal of Korean Library and Information Science Society, 50(4), 339-358. DOI : 10.16981/kliss.50.201912.339