정보관리학회지 (Journal of the Korean Society for information Management)

  • 제39권2호
  • /
  • Pages.299-324
  • /
  • 2022
  • /
  • 1013-0799(pISSN)
  • /
  • 2586-2073(eISSN)
한국정보관리학회 (Korean Society for Information Management)
권호 표지
DOI QR코드

DOI QR Code

도서추천 시스템 개선을 위한 도서이용 맥락 요소 탐색

Exploring the Contextual Elements of Book Use to Improve Book Recommender Systems

  • 심지영 (연세대학교 대학도서관발전연구소)
  • 투고 : 2022.05.20
  • 심사 : 2022.06.11
  • 발행 : 2022.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 기존의 도서추천 시스템 연구에서 간과되어 온 도서이용의 맥락 요소를 파악하기 위해, 다양한 도서탐색 배경을 지닌 적극적인 도서 이용자 15명을 대상으로 6가지 도서탐색 상황에서 생성하는 내용을 사고구술(think-aloud) 프로토콜을 통해 수집하였다. 수집된 도서이용 내용은 내용분석 과정을 통해 독자자문 서비스의 이론적 개념인 '어필 요소(appeal factor)'를 토대로 도서이용에 영향을 미치는 내부 어필 요소와 외부 어필 요소를 각각 식별하였으며, 도서탐색에 사용하는 정보원과 탐색방법 관련 개념들을 또한 세분화하였다. 본 연구의 결과는 향후 도서추천 시스템 설계에 의미 있는 속성 데이터를 추출하고 반영하는 데 사용될 수 있을 것이다.

In this study, in order to explore the contextual elements of book use that were overlooked in the existing book recommender system research, for 15 avid readers with various book search backgrounds, the contents generated in 6 book search situations were collected through the think-aloud protocol. By using content analysis from the collected book use contents, not only the internal and external appeal factors affecting book use, based on the 'appeal factor', the theoretical concept of the readers' advisory service, but also information sources and search methods regarding book use were identified and categorized. The results of this study can be used to extract and reflect meaningful attribute data in the future book recommender system design process.

키워드

과제정보

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2019S1A5B5A07092140).

참고문헌

  1. Ahn, Hee-Jung, Kim, Kee-Won, & Kim Seung-Hoon (2017). Personalized book curation system based on integrated mining of book details and body texts. Journal of Information Technology Applications & Management, 24(1), 33-43. https://doi.org/10.21219/jitam.2017.24.1.033
  2. Cho, Hyun-Yang (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. https://doi.org/10.16981/kliss.48.201706.215
  3. Jung, Youngjin & Cho, Yoonho (2017). Topic modeling-based book recommendations considering online purchase behavior. Knowledge Management Research, 18(4), 97-118. https://doi.org/10.15813/kmr.2017.18.4.004
  4. Ministry of Culture, Sports and Tourism (2019). 2019 Annual Report on Reading Promotion (11-1371000-000162-10).
  5. Son, Jieun, Kim, Seoung-Bum, Kim, Hyunjoong, & Cho, Sungzoon (2015). Review and analysis of recommender systems. Journal of Korean Institute of Industrial Engineers, 41(2), 185-208. https://doi.org/10.7232/JKIIE.2015.41.2.185
  6. Alpert, A. (2006). Incorporating nonfiction into readers' advisory services. Reference & User Services Quarterly, 46(1), 25-32. https://doi.org/10.5860/rusq.46n1.25
  7. Charters, E. (2003). The use of think-aloud methods in qualitative research an introduction to think-aloud methods. Brock Education Journal, 12(2), 68-82. https://doi.org/10.26522/brocked.v12I2.38
  8. Dali, K. (2014). From book appeal to reading appeal: redefining the concept of appeal in readers' advisory. Library Quarterly, 84(1), 22-48. https://doi.org/10.1086/674034
  9. Ettaleb, M., Bellot, P., & Latiri, C. (2020). Mining author-tag multilayer graph for social book search. Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020), 124-132. https://doi.org/10.1142/9789811223334_0016
  10. Flick, U. (2014). An introduction to qualitative research (5h ed.). Los Angeles: Sage Publications Ltd.
  11. Holsti, O. R. (1969). Content analysis for the social sciences and humanities. MA: Addison-Wesley.
  12. Hwang, S. Y. & Lim, E. P. (2002). A data mining approach to new library book recommendations. Proceedings of the 5th International Conference on Asian Digital Libraries, ICADL 2002, 229-240. https://doi.org/10.1007/3-540-36227-4_23
  13. 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 (ICSITech), 246-250. https://doi.org/10.1109/icsitech.2017.8257119
  14. McLean, N. & Davis, J. (2016). Utilising semantically rich big data to enhance book recommendation engines. Proceedings of the 18th IEEE International Conference on High Performance Computing and Communications, the 14th IEEE International Conference on Smart City, and the 2nd IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 1434-1441. https://doi.org/10.1109/hpcc-smartcity-dss.2016.0204
  15. Naik, Y. (2012). Finding good reads on goodreads: readers take ra into their own hands. Reference & User Services Quarterly, 51(4), 319-323. https://doi.org/10.5860/rusq.51n4.319
  16. Ng, Y. K. (2016). Recommending books for children based on the collaborative and content-based filtering approaches. Proceedings of the 16th International Conference on Computational Science and Its Applications, ICCSA 2016, 302-317. https://doi.org/10.1007/978-3-319-42089-9_22
  17. Ooi, K. & Liew, C. L. (2011). Selecting fiction as part of everyday life information seeking. Journal of Documentation, 67(5), 748-772. https://doi.org/10.1108/00220411111164655
  18. Pera, M. S., Condie, N., & Ng, Y. K. (2010). Personalized book recommendations created by using social media data. Proceedings of Web Information Systems Engineering - WISE 2010, 390-403. https://doi.org/10.1007/978-3-642-24396-7_31
  19. Saricks, J. G. (2005). Readers' Advisory Service in the Public Library (3rd ed.). Chicago: American Library Association.
  20. Sariki, T. P. & Kumar, G. B. (2018). A book recommendation system based on named entities. Annals of Library and Information Studies, 65(1), 77-82.
  21. Shangguan, Q., Hu, L., Cao, J., & Xu, G. D. (2012). Book recommendation based on joint multi-relational model. Proceedings of the 2nd International Conference on Cloud and Green Computing, 523-530. https://doi.org/10.1109/cgc.2012.53
  22. Smith, L. C. & Wong, M. A. (2016). Reference and Information Services: An Introduction (5th ed.). Santa Barbara: ABC-CLIO.
  23. Sohail, S. S., Siddiqui, J., & Ali, R. (2013, August 22-25). Book recommendation system using opinion mining technique. Paper presented at the 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Mysore, India, 1609-1614. https://doi.org/10.1109/icacci.2013.6637421
  24. Tewari, A. S. & Barman, A. G. (2016). Collaborative book recommendation system using trust based social network and association rule mining. Proceedings of 2016 International Conference on Contemporary Computing and Informatics (IC3I), 85-88. https://doi.org/10.1109/ic3i.2016.7917939
  25. Tewari, A. S., Ansari, T. S., & Barman, A. G. (2014). Opinion based book recommendation using naive bayes classifier. Proceedings of 2014 International Conference on Contemporary Computing and Informatics (IC3I), 139-144. https://doi.org/10.1109/ic3i.2014.7019672
  26. Tu, Y. F., Chang, S. C., & Hwang, G. J. (2021). Analysing reader behaviours in self-service library stations using a bibliomining approach. Electronic Library, 39(1), 1-16. https://doi.org/10.1108/el-01-2020-0004
  27. Ullah, I. & Khusro, S. (2020). Social book search: the impact of the social web on book retrieval and recommendation. Multimedia Tools and Applications, 79, 8011-8060. https://doi.org/10.1007/s11042-019-08591-0
  28. Walter, F. E., Battiston, S., & Schweitzer, F. (2008). A model of a trust-based recommendation system on a social network. Autonomous Agents and Multi-Agent Systems, 16, 57-74. https://doi.org/10.1007/s10458-007-9021-x
  29. Wang, P. & Soergel, D. (1998). A cognitive model of document use during a research project. study I. document selection. Journal of the American Society for Information Science, 49(2), 115-133. https://doi.org/10.1002/(sici)1097-4571(199802)49:2%3C115::aid-asi3%3E3.0.co;2-t
  30. Wu, F., Hu, Y. H., & Wang, P. R. (2017). Developing a novel recommender network-based ranking mechanism for library book acquisition. Electronic Library, 35(1), 50-68. https://doi.org/10.1108/el-06-2015-0094
  31. Wyatt, N. (2007). The Readers' Advisory Guide to Nonfiction. Chicago: American Library Association.
  32. Zhang, F. L. (2016). A personalized time-sequence-based book recommendation algorithm for digital libraries. IEEE Access, 4, 2714-2720. https://doi.org/10.1109/access.2016.2564997
  33. Zhang, Q. M., Zhu, Y. M., Zang, T. Z., & Yu, J. D. (2020). Learning from multiple graphs of student and book interactions for campus book recommendation. Proceedings of Web Information Systems Engineering - WISE 2020, 316-330. https://doi.org/10.1007/978-3-030-62008-0_22
  34. 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