정보관리학회지 (Journal of the Korean Society for information Management)

  • 제39권3호
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  • Pages.311-336
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  • 2022
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  • 1013-0799(pISSN)
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  • 2586-2073(eISSN)
한국정보관리학회 (Korean Society for Information Management)
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리뷰 정보를 활용한 이용자의 선호요인 식별에 관한 연구

Identification of User Preference Factor Using Review Information

  • 투고 : 2022.08.21
  • 심사 : 2022.09.08
  • 발행 : 2022.09.30
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⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 도서관 정보서비스 환경에서 도서 이용자의 도서추천에 영향을 미치는 선호요인을 파악하기 위해 전 세계 도서 이용자의 참여로 이루어지는 사회적 목록 서비스인 Goodreads 리뷰 데이터를 대상으로 내용분석하였다. 이용자 선호의 내용을 보다 세부적인 관점에서 파악하기 위해 샘플 선정 과정에서 평점 그룹별, 도서별, 이용자별 하위 데이터 집합을 구성하였으며, 다양한 토픽을 고루 반영하기 위해 리뷰 텍스트의 토픽모델링 결과에 기반하여 층화 샘플링을 수행하였다. 그 결과, '내용', '캐릭터', '글쓰기', '읽기', '작가', '스토리', '형식'의 7개 범주에 속하는 총 90개 선호요인 관련 개념을 식별하는 한편, 평점에 따라 드러나는 일반적인 선호요인은 물론 호불호가 분명한 도서와 이용자에서 드러나는 선호요인의 양상을 파악하였다. 본 연구의 결과는 이용자 선호요인의 구체적 양상을 파악하여 향후 추천시스템 등에서 보다 정교한 추천에 기여할 수 있을 것으로 보인다.

This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users' book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories('Content', 'Character', 'Writing', 'Reading', 'Author', 'Story', 'Form') were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.

키워드

과제정보

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2019S1A5B5A07111984).

참고문헌

  1. Byun, Jaeyeon & Shim, Wonsik (2013). A review and application of library user comments data analysis tool: focused on the LibQUAL+ survey comments. Journal of the Korean Society for Information Management, 30(3), 157-181. https://doi.org/10.3743/KOSIM.2013.30.3.157
  2. Choi, Jieun, Ryu, Hyejin, Yu, Dabeen, Kim, Nara, & Kim, Yoonhee (2016). System design for analysis and evaluation of e-commerce products using review sentiment word analysis. Korean Institute of Information Scientists and Engineers Transactions on Computing Practices, 22(5), 209-217.
  3. Kim, Ra Yeon & Park, Eun Gyung (2011). A study on the analysis of essential ingredient for Book Reports. Education Research Studies, 17, 17-30.
  4. Kim, Seonghun, Roh, Yoonju, & 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. https://doi.org/10.16981/kliss.52.3.202109.287
  5. Park, Dong Jin (2010). Condition of writing book reports. Korean Education, 84, 109-125. https://doi.org/10.15734/koed..84.201005.109
  6. Shim, Jiyoung (2020). Exploring the contextual elements of book use to improve book recommender systems. Journal of the Korean Society for Information Management, 39(2), 299-324. https://doi.org/10.3743/KOSIM.2022.39.2.299
  7. Son, Eun-Jeong, Park, Tae-yeon, & Oh, Hyo-Jung (2020). Analysis of utilization status and preference factors of reading room in university library based on user log data: focusing on the case of "J" university. Journal of the Korean Society for Library and Information Science, 54(2), 375-398. https://doi.org/10.4275/KSLIS.2020.54.2.375
  8. Yim, Jungsu (2013). The conjoint analysis of users' preference on the VODs of the newly-released movies. The Journal of the Korea Contents Association, 13(5), 191-198. https://doi.org/10.5392/JKCA.2013.13.05.191
  9. Yoon, Cheong Ok (2012). A study on the user-contributed reviews for the next generation library catalogs. Journal of the Korean Society for Library and Information Science, 46(2), 115-132. https://doi.org/10.4275/KSLIS.2012.46.2.115
  10. Yu, Da-bin, Ryu, Hye-jin, Kim, Na-ra, & Kim, Yoon-hee (2015). A design of a system for customized comparison and evaluation of books using integrated review emotion words analysis. Proceedings of the Korea Information Processing Society Conference, 22(1), 108-111. https://doi.org/10.3745/PKIPS.y2015m04a.108
  11. Begay, W., Lee, D. R., Martin, J., & Ray, M. (2004). Quantifying qualitative data: using LibQUAL+ (TM) comments for library-wide planning activities at the university of arizona. Journal of Library Administration, 40(3-4), 111-119. https://doi.org/10.1300/j111v40n03_09
  12. Bhat, N. A. & Ganai, S. A. (2018). Assessment of user preference to information resources in agricultural libraries in north India. Annals of Library and Information Studies, 65(2), 96-99. https://doi.org/10.56042/alis.v65i2.17377
  13. Chen, L., Chen, G., & Wang, F. (2015). Recommender systems based on user reviews: the state of the art. User Modeling and User-Adapted Interaction, 25(2), 99-154. https://doi.org/10.1007/s11257-015-9155-5
  14. Goodreads (2022, September 5). Public Library Groups. Available: https://www.goodreads.com/group/show_tag/public-library
  15. Green, P. E. & Srinivasan, V. (1990). Conjoint analysis in marketing research: new developments and directions. Journal of Marketing, 54(4), 3-19. https://doi.org/10.2307/1251756
  16. Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017). Bag of tricks for efficient text classification. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2, 427-431. https://doi.org/10.48550/arXiv.1607.01759
  17. Kadiresan, N., Singson, M., & Thiyagarajan, S. (2021). Examining the relationship between academic book citations and Goodreads reader opinion and rating. Annals of Library and Information Studies, 67(4), 215-221. https://doi.org/10.56042/alis.v67i4.32597
  18. Korfiatis, N., Stamolampros, P., Kourouthanassis, P., & Sagiadinos, V. (2019). Measuring service quality from unstructured data: a topic modeling application on airline passengers' online reviews. Expert Systems with Applications, 116, 472-486. https://doi.org/10.1016/j.eswa.2018.09.037
  19. Kousha, K., Thelwall, M., & Abdoli, M. (2017). Goodreads reviews to assess the wider impacts of books. Journal of the Association for Information Science and Technology, 68(8), 2004-2016. https://doi.org/10.1002/asi.23805
  20. Lam, A. H. C., Ho, K. K. W., & Chiu, D. K. W. (2022). Instagram for student learning and library promotions: a quantitative study using the 5E Instructional Model. Aslib Journal of Information Management(ahead-of-print). https://doi.org/10.1108/ajim-12-2021-0389
  21. Liu, H., He, J., Wang, T., Song, W., & Du, X. (2013). Combining user preferences and user opinions for accurate recommendation. Electronic Commerce Research and Applications, 12(1), 14-23. https://doi.org/10.1016/j.elerap.2012.05.002.
  22. 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
  23. McNee, S. M., Riedl, J., & Konstan, J. A. (2006, April 22-27). Being accurate is not enough: how accuracy metrics have hurt recommender systems. Paper presented at the CHI'06 extended abstracts on Human factors in computing systems, Montreal Quebec Canada, 1097-1101. https://doi.org/10.1145/1125451.1125659
  24. Naik, Y. & Trott, B. (2012). Finding good reads on Goodreads: Readers take RA into their own hands. Reference and User Services Quarterly, 51(4), 319-323. https://doi.org/10.5860/rusq.51n4.319
  25. Netzer, O., Toubia, O., Bradlow, E. T., Dahan, E., Evgeniou, T., Feinberg, F. M., Feit, E. M., Hui, S. K., Johnson, J., Liechty, J., Orlin, J. B., & Rao, V. R. (2008). Beyond Conjoint Analysis: Advances in Preference Measurement. Marketing Letters, 19(3/4), 337-354. https://doi.org/10.1007/s11002-008-9046-1
  26. Park, S. (2000). Usability, user preferences, effectiveness, and user behaviors when searching individual and integrated full-text databases: implications for digital libraries. Journal of the American Society for Information Science, 51(5), 456-468. https://doi.org/10.1002/(SICI)1097-4571(2000)51:5<456::AID-ASI6>3.0.CO;2-O
  27. Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020). Stanza: a python natural language processing toolkit for many human languages. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 101-108. https://doi.org/10.48550/arXiv.2003.07082
  28. Reyes, B. M. & Devlin, F. A. (2021). An assessment of e-book collection development practices among romance language librarians. Collection and Curation, 40(1), 24-30. https://doi.org/10.1108/cc-12-2019-0047
  29. Ryan, M. & Farrar, S. (2000). Using conjoint analysis to elicit preferences for health care. Bmj, 320(7248), 1530-1533. https://doi.org/10.1136/bmj.320.7248.1530
  30. Shah, A. M., Yan, X., Tariq, S., & Ali, M. (2021). What patients like or dislike in physicians: analyzing drivers of patient satisfaction and dissatisfaction using a digital topic modeling approach. Information Processing & Management, 58(3), 102516. https://doi.org/10.1016/j.ipm.2021.102516
  31. 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, Mysore, India, 1609-1614. https://doi.org/10.1109/icacci.2013.6637421
  32. Sun, L., Chen, J., Li, J., & Peng, Y. (2015). Joint topic-opinion model for implicit feature extracting. Proceedings of 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, 208-213. https://doi.org/10.1109/iske.2015.17
  33. Thelwall, M. & Kousha, K. (2017). Goodreads: A social network site for book readers. Journal of the Association for Information Science and Technology, 68(4), 972-983. https://doi.org/10.1002/asi.23733
  34. 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
  35. Tubishat, M., Idris, N., & Abushariah, M. A. (2018). Implicit aspect extraction in sentiment analysis: review, taxonomy, oppportunities, and open challenges. Information Processing & Management, 54(4), 545-563. https://doi.org/10.1016/j.ipm.2018.03.008
  36. Wan, M., Misra, R., Nakashole, N., & McAuley, J. (2019). Fine-grained spoiler detection from large-scale review corpora. Proceedings of the 2019 57th Conference of the Association for Computational Linguistics, 2605-2610. https://doi.org/10.48550/arXiv.1905.13416
  37. Wilson, K. M., Hooper, R., Simpson, J., & Slay, J. (2021). Comparing print and ebook usage to meet patron needs. Collection Management, 46(2), 91-106. https://doi.org/10.1080/01462679.2020.1833802
  38. Wu, D., He, D., Qiu, J., Lin, R., & Liu, Y. (2013). Comparing social tags with subject headings on annotating books: a study comparing the information science domain in English and Chinese. Journal of Information Science, 39(2), 169-187. https://doi.org/10.1177/0165551512451808
  39. 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
  40. Xiao, S., Wei, C. P., & Dong, M. (2016). Crowd intelligence: analyzing online product reviews for preference measurement. Information & Management, 53(2), 169-182. https://doi.org/10.1016/j.im.2015.09.010