[논문]도서추천 시스템 개선을 위한 도서이용 맥락 요소 탐색

심지영

doi:10.3743/kosim.2022.39.2.299

[국내논문] 도서추천 시스템 개선을 위한 도서이용 맥락 요소 탐색
Exploring the Contextual Elements of Book Use to Improve Book Recommender Systems 원문보기

정보관리학회지 = Journal of the Korean society for information management, v.39 no.2, 2022년, pp.299 - 324

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (8)

표 <표 1> 선행연구에서 언급된 도서이용 맥락 요소*
그림 <그림 1> 도서탐색 시 주로 이용하는 정보원* (n=15) * 도서탐색 시 주로 이용하는 정보원 항목은 문화체육관광부(2019, 60) '독서진흥에 관한 연차보고서'의 도서 정보원 설문 항목을 토대로 재구성함
표 <표 2> 도서탐색 실험 및 인터뷰 참여자 구성
표 <표 3> 도서탐색 및 선택 시 고려하는 요소
표 <표 4> 도서이용에 영향을 미치는 내부 어필 요소(Internal Appeal Factor)
표 <표 5> 도서이용에 영향을 미치는 외부 어필 요소(External Appeal Factor)
표 <표 6> 도서탐색에 사용하는 정보원
표 <표 7> 도서이용에 사용하는 탐색방법

참고문헌 (34)

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

원문보기 상세보기
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

원문보기 상세보기
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

원문보기 상세보기
Ministry of Culture, Sports and Tourism (2019). 2019 Annual Report on Reading Promotion (11-1371000-000162-10).
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

원문보기 상세보기
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

상세보기
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

상세보기
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

상세보기
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
Flick, U. (2014). An introduction to qualitative research (5h ed.). Los Angeles: Sage Publications Ltd.
Holsti, O. R. (1969). Content analysis for the social sciences and humanities. MA: Addison-Wesley.
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

상세보기
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
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
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

상세보기
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
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

상세보기
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

상세보기
Saricks, J. G. (2005). Readers' Advisory Service in the Public Library (3rd ed.). Chicago: American Library Association.
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.
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
Smith, L. C. & Wong, M. A. (2016). Reference and Information Services: An Introduction (5th ed.). Santa Barbara: ABC-CLIO.
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
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 (IC 3 I), 85-88. https://doi.org/10.1109/ic3i.2016.7917939
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 (IC 3 I), 139-144. https://doi.org/10.1109/ic3i.2014.7019672
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

상세보기
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

상세보기
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

상세보기
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

상세보기
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

상세보기
Wyatt, N. (2007). The Readers' Advisory Guide to Nonfiction. Chicago: American Library Association.
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

상세보기
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
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

상세보기

저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증