참고문헌 (43)

1. Aharony, N. (2012). An analysis of American academic libraries' websites: 2000-2010. The Electronic Library, 30(6), 764-776. 

   상세보기

2. Barba, I., Cassidy, R., De Leon, E., & Williams, B. J. (2013). Web analytics reveal user behavior: TTU libraries' experience with google analytics. Journal of Web Librarianship, 7(4), 389-400. 

   상세보기

3. Bradley, S. (2011). 3 Design layouts: Gutenberg diagram, Z-pattern, and F-pattern. Vanseo Design. Retrieved October 21, 2018 from https://vanseodesign.com/webdesign/3-design-layouts/. 

4. Callan, J., Smeaton, A., Beaulieu, M., Borlund, P., Brusilovsky, P., Chalmers, M., . . . Toms, E. (2003). Personalisation and recommender systems in digital libraries (Joint NSFEU DELOS Working Group Report). Retrieved October 21, 2018 from http://www.ercim.org/publication/ws-proceedings/Delos-NSF/Personalisation.pdf. 

5. Choo, C. W., Detlor, B., & Turnbull, D. (2000). Information seeking on the web: An integrated model of browsing and searching. First Monday, 5(2). doi:10.5210/fm.v5i2.729. 

   상세보기

6. Clark, D. J., Nicholas, D., & Jamali, H. R. (2014). Evaluating information seeking and use in the changing virtual world: The emerging role of Google Analytics. Learned Publishing, 27(3), 185-194. 

   상세보기

7. Clifton, B. (2012). Advanced web metrics with Google Analytics. Indianapolis: John Wiley and Sons. 

8. Esposito, A., Faundez-Zanuy, M., Morabito, F. C., & Pasero, E. (2019). A human-centered behavioral informatics. In A. Esposito, M. Faundez-Zanuy, F. C. Morabito, & E. Pasero (Eds.), Quantifying and processing biomedical and behavioral signals (pp. 3-8). Cham: Springer. 

9. Fablinova, O. N. (2015). Internet behavior as an object of study of social sciences. Sociological Miscellany, 6, 543-549. Retrieved October 21, 2018 from http://socio.bas-net.by/wp-content/uploads/2016/04/soc_alm6.pdf. 

10. Fang, W. (2007). Using Google Analytics for improving library website content and design: A case study. Library Philosophy and Practice, 9(2), 22. 

11. Frias-Martinez, E., Chen, S. Y., & Liu, X. (2007). Automatic cognitive style identification of digital library users for personalization. Journal of the American Society for Information Science and Technology, 58(2), 237-251. 

    상세보기

12. Goncalves, M., Rocha, T., Magalhaes, L., Peres, E., Bessa, M., & Chalmers, A. (2013). Identifying different visual patterns in web users behaviour. Proceedings of the 27th Spring Conference on Computer Graphics (pp. 65-70). New York: ACM. 

13. He, B., & Zhang, H. Y. (2016). Research on personalized information recommendation of library. Proceeding of the 2016 4th International Conference on Machinery, Materials and Computing Technology; Advances in Engineering Research (pp. 1251-1254). Hangzhou: Atlantis Press. 

14. Ji, H., Yun, Y., Lee, S., Kim, K., & Lim, H. (2017). An adaptable UI/UX considering user's cognitive and behavior information in distributed environment. Cluster Computing, 21(1), 1045-1058. 

15. Kellar, M., Watters, C., & Shepherd, M. (2007). A field study characterizing Web-based information-seeking tasks. Journal of the American Society for Information Science and Technology, 58(7), 999-1018. 

    상세보기

16. Kotyak, E., & Narizhnyi, D. (2016). How users see websites: Fand Z- patterns, Gutenberg diagram. Retrieved December 22, 2018 from https://netology.ru/blog/users-site-patterns. 

17. Lindley, S. E., Meek, S., Sellen, A., & Harper, R. (2012). "It's simply integral to what I do": enquiries into how the web is weaved into everyday life. Proceedings of the 21st international conference on World Wide Web (pp. 1067-1076). Lyon: ACM. 

18. Loftus, W. (2012). Demonstrating success: Web analytics and continuous improvement. Journal of Web Librarianship, 6(1), 45-55. 

    상세보기

19. Morrison, J. B., Pirolli, P., & Card, S. K. (2001). A taxonomic analysis of what World Wide Web activities significantly impact people's decisions and actions. Proceedings of Extended Abstracts on Human Factors in Computing Systems (pp. 163-164). Seattle: ACM. 

20. Narayanan, S., & Georgiou, P. (2013). Behavioral signal processing: Deriving human behavioral informatics from speech and language: computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and disordered-from speech and language cues and their applications in health, commerce, education, and beyond. Proceedings of the IEEE, 101(5), 1203-1233. 

    상세보기

21. Nielsen, J. (2006). F-Shaped pattern for reading web content (original study). Retrieved December 22, 2018 from https://www.nngroup.com/articles/f-shaped-pattern-reading-webcontent-discovered/. 

22. Nunez-Valdez, E. R., Quintana, D., Crespo, R. G., Isasi, P., & Herrera-Viedma, E. (2018). A recommender system based on implicit feedback for selective dissemination of ebooks. Information Sciences, 467, 87-98. 

    상세보기

23. Ortiz-Cordova, A., & Jansen, B. J. (2012). Classifying web search queries to identify high revenue generating customers. Journal of the American Society for Information Science and Technology, 63(7), 1426-1441. 

    상세보기

24. Pernice, K. (2017). F-Shaped pattern of reading on the web: Misunderstood, but still relevant (even on mobile). Retrieved December 22, 2018 from https://www.nngroup.com/articles/f-shaped-pattern-reading-web-content. 

25. Pernice, K., Whitenton, K., & Nielsen, J. (2014). How people read on the web: The eyetracking evidence. Fremont: Nielsen Norman Group. 

26. Redkina, N. S. (2014). The quality of online library services. Scientific and Technical Libraries, 8, 18-27. Retrieved October 21, 2018 from http://www.gpntb.ru/ntb/ntb/2014/8/ntb_8_2_2014.pdf. 

27. Rosenfeld, L., Morville, P., & Arango, J. (2015). Information architecture: For the web and beyond. Sebastopol: O'Reilly. 

28. Rozanski, H. D., Bollman, G., & Lipman, M. (2001) Seize the occasion! The seven-segment system for online marketing. Strategy+business, (24). Retrieved October 21, 2018 from https://www.strategy-business.com/article/19940?gkod29c9. 

29. Rykhtorova, A. E., & Udartseva, O. M. (2018). Website users' segmentation to promote library resources and services. Biblosphera, 3, 59-67. 

30. Sellen, A. J., Murphy, R., & Shaw, K. L. (2002). How knowledge workers use the web. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 227-234). Minneapolis: ACM. 

31. Shevchenko, L. B. (2019). Technology of recommendations as a means of personalizing library websites. Information Resources of Russia, 2, 14-16. Retrieved December 22, 2018 from http://www.rosenergo.gov.ru/information_and_analytical_support/informatsionnie_resursi_rossii/informatsionnie_resursi_rossii_2_168__2019. 

32. Smith, M., Kukulska-Hulme, A., & Page, A. (2012). Educational use cases from a shared exploration of e-books and iPads. In: T. T., Goh, (Ed.), E-books and E-readers for E-learning (pp. 25-53). Wellington: Victoria Business School, Victoria University of Wellington. 

33. Tobias, C., & Blair, A. (2015). Listen to what you cannot hear, observe what you cannot see: An introduction to evidence-based methods for evaluating and enhancing the user experience in distance library services. Journal of Library & Information Services in Distance Learning, 9(1-2), 148-156. 

    

34. Tolstikov, A., Shakhray, M., Gusev, G., & Serdyukov, P. (2014). How user behavior on the website may affect the ranking. Yandex research. Retrieved December 22, 2018 from https://siteclinic.ru/blog/web-analytics/analiz-povedeniyapolzovatelya-yandex. 

35. Turner, S. J. (2010). Website statistics 2.0: Using Google Analytics to measure library website effectiveness. Technical Services Quarterly, 27(3), 261-278. 

    상세보기

36. Udartseva, O. M., & Rykhtorova, A. E. (2018). Using web analytics tools in evaluating the effectiveness of ways to promote library resources. Biblosphera, 2, 93-99. 

37. Vecchione, A., Brown, D., Allen, E., & Baschnagel, A. (2016). Tracking user behavior with Google Analytics events on an academic library web site. Journal of Web Librarianship, 10(3), 161-175. 

    상세보기

38. Wakeling, S. (2015). Establishing user requirements for a recommender system in an online union catalogue: An investigation of WorldCat.org. Retrieved October 21, 2018 from http://etheses.whiterose.ac.uk/9163/7/Simon%20Wakeling%20Final%20Thesis%20Submitted%202.pdf. 

39. Wilson, I., & Mukhina, M. (2012). Market segmentation in Russian subsidiaries of FMCG MNEs: Practitioner and academic perspectives. Marketing Intelligence & Planning, 30(1), 53-68. 

    상세보기

40. Xu, C. (2017). A personalized recommender system based on library database. International Journal of Emerging Technologies in Learning, 12(12), 134-141. 

    상세보기

41. Yang, L., & Perrin, J. M. (2014). Tutorials on Google Analytics: How to craft a Web Analytics report for a library web site. Journal of Web Librarianship, 8(4), 404-417. 

    상세보기

42. Yang, S. Q., & Dalal, H. A. (2015). Delivering virtual reference services on the web: An investigation into the current practice by academic libraries. The Journal of Academic Librarianship, 41(1), 68-86. 

    상세보기

43. Yi, K., Chen, T., & Cong, G. (2018). Library personalized recommendation service method based on improved association rules. Library Hi Tech, 36(3), 443-457. 

    상세보기