DOI QR코드

DOI QR Code

A Study on the Development of the School Library Book Recommendation System Using the Association Rule

연관규칙을 활용한 학교도서관 도서추천시스템 개발에 관한 연구

  • Received : 2022.08.08
  • Accepted : 2022.09.08
  • Published : 2022.09.30

Abstract

The purpose of this study is to propose a book recommendation system that can be used in school libraries. The book recommendation system applies an algorithm based on association rules using DLS lending data and is designed to provide personalized book recommendation services to school library users. For this purpose, association rules based on the Apriori algorithm and betweenness centrality analysis were applied and detailed functions such as descriptive statistics, generation of association rules, student-centered recommendation, and book-centered recommendation were materialized. Subsequently, opinions on the use of the book recommendation system were investigated through in-depth interviews with teacher librarians. As a result of the investigation, opinions on the necessity and difficulty of book recommendation, student responses, differences from existing recommendation methods, utilization methods, and improvements were confirmed and based on this, the following discussions were proposed. First, it is necessary to provide long-term lending data to understand the characteristics of each school. Second, it is necessary to discuss the data integration plan by region or school characteristics. Third, It is necessary to establish a book recommendation system provided by the Comprehensive Support System for Reading Education. Based on the contents proposed in this study, it is expected that various discussions will be made on the application of a personalization recommendation system that can be used in the school library in the future.

본 연구는 학교도서관에서 활용할 수 있는 도서추천시스템을 제안하는데 목적이 있다. 도서추천시스템은 DLS의 대출 데이터를 활용하여 연관규칙 기반의 알고리즘을 적용하였으며, 학교도서관 이용자들에게 개인화 도서추천 서비스 제공이 가능하도록 설계하였다. 이를 위해 Apriori 알고리즘 기반의 연관규칙과 매개 중심성 분석을 적용하고, 기술통계, 연관규칙 생성, 학생중심 추천, 도서 중심추천 등 세부 기능을 구현하였다. 이어서 사서교사를 대상으로 심층면담을 통해 도서추천시스템 사용에 대한 의견을 조사하였다. 조사 결과, 도서추천의 필요성 및 어려움, 학생의 반응, 기존 추천방식과의 차이점 및 활용방안, 개선 사항에 대한 의견을 확인할 수 있었으며, 이를 토대로 다음의 논의점을 제안하였다. 첫째, 개별학교의 특성을 파악하기 위해서 장기간의 대출 데이터의 제공이 필요하다. 둘째, 지역별 혹은 학교 특성별 데이터 통합 방안에 대한 논의가 필요하다. 셋째, 독서교육종합시스템에서 제공하는 도서추천시스템의 구축이 필요하다. 본 연구에서 제안된 내용을 토대로 향후 학교도서관 현장에서 활용할 수 있는 개인화 추천시스템 적용에 대한 다양한 논의가 이루어지길 기대한다.

Keywords

References

  1. Byun, Woo-Yeoul & Lee, Mihwa (2017). A study on the improvement of digital library system for school library. Journal of the Korean Society for Information Management, 34(1), 31-50. http://dx.doi.org/10.3743/KOSIM.2017.34.1.031
  2. Chung, Young-Mee & Lee, Yong-Gu (2002). Developing a book recommendation system using filtering techniques. Journal of Information Management, 33(1), 1-17. https://doi.org/10.1633/JIM.2002.33.1.001
  3. Hong, Yeon-kyoung, Jeon, Seo-young, Choi, Jae-young, Yang, Hee-yoon, Han, Chae-eun, & Zhu, Yongjun (2021). A study on the development and evaluation of personalized book recommendation systems in university libraries based on individual loan records. Journal of the Korean Society for Information Management, 38(2), 113-127. http://10.3743/KOSIM.2021.38.2.113
  4. Jang, Yoon-geum, Mo, Young-gyu, Kim, Se-hoon, Lee, Hye-eun, Jeon, Gyeong-sun, Lee, Hye-young, & Lee, Eun-ji (2018). A Study To Establish a Comprehensive Plan for University Library Promotion (2019-2023)(CR 2018-3). Korea Education and Research Information Service.
  5. Jung, Kyung-Yong, Kim, Jin-Hyun, Jung, Heon-Man, & Lee, Jung-Hyun (2004). An item-based collaborative filtering technique by associative relation clustering in personalized recommender systems. Journal of Korean Institute of Information Scientists and Engineers: Software and Applications, 31(4), 467-477.
  6. Kang, Bong-Suk & Jung, Youngmi (2019). Discussions on the accessibility of school library dls catalogue records: focused on literary collections. Journal of Korean Library and Information Science Society, 50(4), 539-559. http://dx.doi.org/10.16981/kliss.50.4.201912.539
  7. Kim, Hye-sun, Lee, Tae-seok, Kim, Seon-tae, Shin, Su-mi, Kim, Wan-jong, Lee, Hye-jin, Hyun, Mi-hwan, Baek, Jong-myung, & Lee, Eun-ji (2015). Construction of Library Big Data Analysis and Utilization System (2014) Final Report. Korea Institute of Science and Technology Information.
  8. Kim, Ji-Hye & Park, Doo-Soon (2006). Development of the goods recommendation system using association rules and collaborating filtering. The Korean Association of Computer Education, 9(1), 71-80.
  9. Kim, Mi-Sung, Kim, Namgyu, & Ahn, Jae-Hyeon (2012). An investigation on expanding co-occurrence criteria in association rule mining. Journal of Intelligence and Information Systems, 18(1), 23-38. https://doi.org/10.13088/JIIS.2012.18.1.023
  10. Kim, Seong-Hun, Roh, Yoon-Ju, & Kim, Mi-Ryung (2021). A narrative study on user satisfaction of book recommendation service based on association analysis. Journal of Korean Library and Information Science Society, 52(3), 287-311. http://dx.doi.org/10.16981/kliss.52.3.202109.287
  11. Kim, Yong & Moon, Sung Been (2006). A study on hybrid recommendation system based on usage frequency for multimedia contents. Journal of the Korean Society for Information Management, 23(3), 91-126. https://doi.org/10.3743/KOSIM.2006.23.3.091
  12. Kim, Yong (2012). A study on design and implementation of personalized information recommendation system based on Apriori algorithm. Journal of the Korean Biblia Society for Library and Information Science, 23(4), 283-308. http://dx.doi.org/10.14699/kbiblia.2012.23.4.283
  13. Kwahk, Kee-Young (2019). Social Network Analysis. Seoul: Cheongram.
  14. Lee, Hyeonsook & Lee, Yong-Jae (2022). A study on management and improvement of school libraries with viewpoint of five laws of library science: focused on d elementary school library in Busan. Journal of Korean Library and Information Science Society, 53(1), 171-190. http://dx.doi.org/10.16981/kliss.53.1.202203.171
  15. Lee, Jaesik & Myung, Hoonshik (2008). Development of a book recommender system for internet bookstore using case-based reasoning. The Journal of Society for e-Business Studies, 13(4), 173-191.
  16. Moon, Hyun Sil, Lim, Jin Hyuk, Kim, Doyeon, & Cho, YoonHo (2020). A deep learning based recommender system using visual information. Knowledge Management Review, 21(3), 24-44. http://doi.org/10.15813/kmr.2020.21.3.002
  17. Park, Dae-Woo, Koh, In Soo, Lee, Nak-Son, & Han, Kyeong-Seok (2020). A study on architecture for bigdata-based book curation system. The Korea Society of Information Technology Policy & Management, 12(1), 1559-1565.
  18. Park, Jong-Hak, Cho, Yoon-Ho, & Kim, Jae-Kyeong (2009). Social network: a novel approach to new customer recommendations. Korea Intelligent Information System Society, 15(1), 123-140.
  19. Pei, Yun-feng, Sohn, Jong-soo, Wang, Qing, Song, Tae-sung, & Chung, In-jeong (2011). Contents recommendation method using betweenness centrality analysis. Proceedings of the Korean Society for Inernet Information 2011 Summer Conference, 12(1), 167-168.
  20. Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. of the 20th International Conference on Very Large DataBases, Santiage, Chile, 487-499.
  21. Ahn, H. J. (2008). A New similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37-51. https://doi.org/10.1016/j.ins.2007.07.024
  22. Balabanovic, M. & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72. http://doi.org/10.1145/245108.245124
  23. Freeman, L. C. (1979). Centrality in social networks: conceptual clarification. Social Networks, 1, 215-239. http://doi.org/10.1016/0378-8733(78)90021-7
  24. Mariana, S., Surjandari, I., Dhini, A., Rosyidah, A., & Prameswari, P. (2017). Association rule mining for building book recommendation system in online public access catalog. Proceedings of the 2017 3rd International Conference on Science in Information Technology, 246-250. https://doi.org/10.1109/icsitech.2017.8257119
  25. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithm. Proceedings of the 10th International Conference on World Wide Web, 285-295. http://doi.org/10.1145/371920.372071
  26. Wu, Y. H. & Chen, A. L. (2000). Index struc tures of user profiles for efficient web page filtering services. Proceedings of Institute of Electrical and Electronics Engineers Conference on Distributed Computing Systems, 644-651. http://doi.org/10.1109/ICDCS.2000.840981
  27. Ziegler, S. & Shrake, R. (2018). PAL: toward a recommendation system for manuscripts. Information Technology and Libraries, 37(3), 84-98. https://doi.org/10.6017/ital.v37i3.10357