Research Article
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Geçici Bilgi İhtiyacının Giderilme Sürecinde Kullanıcı Okuma Davranışlarının İncelenmesi

Year 2021, Volume 35, Issue 4, 547 - 566, 31.12.2021
https://doi.org/10.24146/tk.955630

Abstract

Kısa süreli/geçici olarak ortaya çıkan bilgi ihtiyacımızı gidermek amacıyla sıklıkla başvurduğumuz arama motorları gibi internet tabanlı araçlar sayesinde, bilgiye erişme işlemi hız ve pratiklik kazanmıştır. Bu tür araçlar literatürde “Bilgi Erişim Sistemleri” kategorisinde değerlendirilen sistemlerdir. Bu çalışmada, Kırklareli Üniversitesi web sitelerinde arama motorlarına benzer şekilde görev yapan site-içi arama araçlarında, kullanıcıların geçici bilgi ihtiyaçlarını giderirken gerçekleştirdikleri arama etkileşimlerinin incelenmesi ve kullanıcıların okuma davranışlarının ortaya çıkartılması amaçlanmıştır. Mann-Whitney U testi ile ortaya çıkarılan sonuçlar, kullanıcıların ilk kez karşılaştıkları sayfalarda daha fazla zaman geçirdiklerini ortaya çıkarmıştır (p<0,05). Aynı sayfalara yapılan ikinci ziyaretlerde ise hızlı bir şekilde sayfanın taranarak bilgi ihtiyacının giderildiği belirlenmiştir (p<0,05). Ek olarak, Spearman Korelasyon test sonuçları, ilk ya da ikinci defa okunan sayfalarda, kelime miktarı ile kullanıcıların kelime başına okuma süreleri arasındaki negatif yönde bir ilişki olduğunu ortaya çıkarmıştır (p<0,05). Çalışmada ortaya çıkarılmış olan tüm bulgular detaylı bir şekilde açıklanmış olup, bu bulgular üzerinden Bilgi Erişim Sistemleri’nin daha iyi hizmet vermesine yönelik nelere dikkat edilebileceği üzerine öneriler getirilmiştir.

References

  • Agichtein, E., Brill, E. ve Dumais, S. (2019). Improving Web Search Ranking by Incorporating User Behavior Information. ACM SIGIR Forum, 52(2), 11-18. doi: 10.1145/3308774.3308778
  • Akuma, S., Iqbal, R., Jayne, C. ve Doctor, F. (2016). Comparative analysis of relevance feedback methods based on two user studies. Computers in Human Behavior, 60, 138-146. doi: 10.1016/j.chb.2016.02.064
  • Akuma, S., Jayne, C., Iqbal, R. ve Doctor, F. (2014). Implicit Predictive Indicators: Mouse Activity and Dwell Time. Artificial Intelligence Applications and Innovations, 162-171. Berlin, Heidelberg: Springer. doi: 10.1007/978-3-662-44654-6_16
  • Baeza-Yates, R. ve Ribeiro-Neto, B. (2011). Modern Information Retrieval: The concepts and technology behind search. USA: Addison-Wesley Publishing Company.
  • Balakrishnan, V. ve Zhang, X. (2014). Implicit user behaviours to improve post-retrieval document relevancy. Computers in Human Behavior, 33, 104-112. doi: 10.1016/j.chb.2014.01.001
  • Croft, B., Metzler, D. ve Strohman, T. (2009). Search Engines: Information Retrieval in Practice. Boston: Pearson.
  • Guo, Q. ve Agichtein, E. (2012). Beyond dwell time: Estimating document relevance from cursor movements and other post-click searcher behavior. Proceedings of the 21st international conference on World Wide Web, 569-578. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/2187836.2187914
  • Internet Live Stats. (2021, 08 Haziran). Google Search Statistics—Internet Live Stats. Erişim Adresi: https://www.internetlivestats.com/google-search-statistics/
  • Kelly, D. ve Belkin, N. J. (2001). Reading time, scrolling and interaction: Exploring implicit sources of user preferences for relevance feedback. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 408-409. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/383952.384045
  • Kelly, D. ve Belkin, N. J. (2004). Display time as implicit feedback: Understanding task effects. Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 377-384. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/1008992.1009057
  • Kim, J. Y., Teevan, J. ve Craswell, N. (2016). Explicit In Situ User Feedback for Web Search Results. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 829-832. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/2911451.2914754
  • Kim, Y., Hassan, A., White, R. W. ve Zitouni, I. (2014). Modeling dwell time to predict click-level satisfaction. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 193-202. New York New York USA: ACM. doi: 10.1145/2556195.2556220
  • Kowalski, G. (2011). Information Retrieval Architecture and Algorithms. Boston, MA: Springer US. doi: 10.1007/978-1-4419-7716-8
  • Kwok, K.-L., Grunfeld, L. ve Deng, P. (2007). Employing web mining and data fusion to improve weak ad hoc retrieval. Information Processing & Management, 43(2), 406-419. doi: 10.1016/j.ipm.2006.07.008
  • Liu, J. ve Belkin, N. J. (2015). Personalizing information retrieval for multi-session tasks: Examining the roles of task stage, task type, and topic knowledge on the interpretation of dwell time as an indicator of document usefulness. Journal of the Association for Information Science and Technology, 66(1), 58-81. doi: 10.1002/asi.23160
  • Manning, C. D., Raghavan, P. ve Schütze, H. (2008). Introduction to information retrieval. New York: Cambridge University Press.
  • Mao, J., Liu, Y., Luan, H., Zhang, M., Ma, S., Luo, H. ve Zhang, Y. (2017). Understanding and Predicting Usefulness Judgment in Web Search. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1169-1172. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/3077136.3080750
  • Morita, M. ve Shinoda, Y. (1994). Information filtering based on user behavior analysis and best match text retrieval. Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, 272-281. Berlin, Heidelberg: Springer-Verlag.
  • Núñez-Valdéz, E. R., Cueva Lovelle, J. M., Sanjuán Martínez, O., García-Díaz, V., Ordoñez de Pablos, P. ve Montenegro Marín, C. E. (2012). Implicit feedback techniques on recommender systems applied to electronic books. Computers in Human Behavior, 28(4), 1186-1193. doi: 10.1016/j.chb.2012.02.001
  • Statista. (2021, 08 Haziran). Search engine market share worldwide. Erişim Adresi: https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/
  • Uçak, N. Ö. ve Güzeldere, Ş. O. (2006). Bilişsel Yapının ve İşlemlerin Bilgi Arama Davranışı Üzerine Etkisi. Türk Kütüphaneciliği, 20(1), 7-28.
  • White, R. W. ve Kelly, D. (2006). A study on the effects of personalization and task information on implicit feedback performance. Proceedings of the 15th ACM international conference on Information and knowledge management, 297-306. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/1183614.1183659
  • Xu, S., Jiang, H. ve Lau, F. C. M. (2011). Mining user dwell time for personalized web search re-ranking. Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three, 2367-2372. Barcelona, Catalonia, Spain: AAAI Press.
  • Yi, X., Hong, L., Zhong, E., Liu, N. N. ve Rajan, S. (2014). Beyond clicks: Dwell time for personalization. Proceedings of the 8th ACM Conference on Recommender Systems - RecSys ’14, 113-120. Foster City, Silicon Valley, California, USA: ACM Press. doi: 10.1145/2645710.2645724
  • Zhai, C. ve Massung, S. (2016). Text data management and analysis: A practical introduction to information retrieval and text mining. New York: Association for Computing Machinery.

Year 2021, Volume 35, Issue 4, 547 - 566, 31.12.2021
https://doi.org/10.24146/tk.955630

Abstract

References

  • Agichtein, E., Brill, E. ve Dumais, S. (2019). Improving Web Search Ranking by Incorporating User Behavior Information. ACM SIGIR Forum, 52(2), 11-18. doi: 10.1145/3308774.3308778
  • Akuma, S., Iqbal, R., Jayne, C. ve Doctor, F. (2016). Comparative analysis of relevance feedback methods based on two user studies. Computers in Human Behavior, 60, 138-146. doi: 10.1016/j.chb.2016.02.064
  • Akuma, S., Jayne, C., Iqbal, R. ve Doctor, F. (2014). Implicit Predictive Indicators: Mouse Activity and Dwell Time. Artificial Intelligence Applications and Innovations, 162-171. Berlin, Heidelberg: Springer. doi: 10.1007/978-3-662-44654-6_16
  • Baeza-Yates, R. ve Ribeiro-Neto, B. (2011). Modern Information Retrieval: The concepts and technology behind search. USA: Addison-Wesley Publishing Company.
  • Balakrishnan, V. ve Zhang, X. (2014). Implicit user behaviours to improve post-retrieval document relevancy. Computers in Human Behavior, 33, 104-112. doi: 10.1016/j.chb.2014.01.001
  • Croft, B., Metzler, D. ve Strohman, T. (2009). Search Engines: Information Retrieval in Practice. Boston: Pearson.
  • Guo, Q. ve Agichtein, E. (2012). Beyond dwell time: Estimating document relevance from cursor movements and other post-click searcher behavior. Proceedings of the 21st international conference on World Wide Web, 569-578. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/2187836.2187914
  • Internet Live Stats. (2021, 08 Haziran). Google Search Statistics—Internet Live Stats. Erişim Adresi: https://www.internetlivestats.com/google-search-statistics/
  • Kelly, D. ve Belkin, N. J. (2001). Reading time, scrolling and interaction: Exploring implicit sources of user preferences for relevance feedback. Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, 408-409. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/383952.384045
  • Kelly, D. ve Belkin, N. J. (2004). Display time as implicit feedback: Understanding task effects. Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, 377-384. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/1008992.1009057
  • Kim, J. Y., Teevan, J. ve Craswell, N. (2016). Explicit In Situ User Feedback for Web Search Results. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 829-832. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/2911451.2914754
  • Kim, Y., Hassan, A., White, R. W. ve Zitouni, I. (2014). Modeling dwell time to predict click-level satisfaction. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 193-202. New York New York USA: ACM. doi: 10.1145/2556195.2556220
  • Kowalski, G. (2011). Information Retrieval Architecture and Algorithms. Boston, MA: Springer US. doi: 10.1007/978-1-4419-7716-8
  • Kwok, K.-L., Grunfeld, L. ve Deng, P. (2007). Employing web mining and data fusion to improve weak ad hoc retrieval. Information Processing & Management, 43(2), 406-419. doi: 10.1016/j.ipm.2006.07.008
  • Liu, J. ve Belkin, N. J. (2015). Personalizing information retrieval for multi-session tasks: Examining the roles of task stage, task type, and topic knowledge on the interpretation of dwell time as an indicator of document usefulness. Journal of the Association for Information Science and Technology, 66(1), 58-81. doi: 10.1002/asi.23160
  • Manning, C. D., Raghavan, P. ve Schütze, H. (2008). Introduction to information retrieval. New York: Cambridge University Press.
  • Mao, J., Liu, Y., Luan, H., Zhang, M., Ma, S., Luo, H. ve Zhang, Y. (2017). Understanding and Predicting Usefulness Judgment in Web Search. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1169-1172. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/3077136.3080750
  • Morita, M. ve Shinoda, Y. (1994). Information filtering based on user behavior analysis and best match text retrieval. Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, 272-281. Berlin, Heidelberg: Springer-Verlag.
  • Núñez-Valdéz, E. R., Cueva Lovelle, J. M., Sanjuán Martínez, O., García-Díaz, V., Ordoñez de Pablos, P. ve Montenegro Marín, C. E. (2012). Implicit feedback techniques on recommender systems applied to electronic books. Computers in Human Behavior, 28(4), 1186-1193. doi: 10.1016/j.chb.2012.02.001
  • Statista. (2021, 08 Haziran). Search engine market share worldwide. Erişim Adresi: https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/
  • Uçak, N. Ö. ve Güzeldere, Ş. O. (2006). Bilişsel Yapının ve İşlemlerin Bilgi Arama Davranışı Üzerine Etkisi. Türk Kütüphaneciliği, 20(1), 7-28.
  • White, R. W. ve Kelly, D. (2006). A study on the effects of personalization and task information on implicit feedback performance. Proceedings of the 15th ACM international conference on Information and knowledge management, 297-306. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/1183614.1183659
  • Xu, S., Jiang, H. ve Lau, F. C. M. (2011). Mining user dwell time for personalized web search re-ranking. Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three, 2367-2372. Barcelona, Catalonia, Spain: AAAI Press.
  • Yi, X., Hong, L., Zhong, E., Liu, N. N. ve Rajan, S. (2014). Beyond clicks: Dwell time for personalization. Proceedings of the 8th ACM Conference on Recommender Systems - RecSys ’14, 113-120. Foster City, Silicon Valley, California, USA: ACM Press. doi: 10.1145/2645710.2645724
  • Zhai, C. ve Massung, S. (2016). Text data management and analysis: A practical introduction to information retrieval and text mining. New York: Association for Computing Machinery.

Details

Primary Language Turkish
Subjects Information Science and Library Science
Journal Section Research Articles
Authors

Veli Özcan BUDAK (Primary Author)
Kırklareli Üniversitesi
0000-0002-0960-0542
Türkiye

Early Pub Date November 19, 2021
Publication Date December 31, 2021
Application Date June 21, 2021
Acceptance Date October 25, 2021
Published in Issue Year 2021, Volume 35, Issue 4

Cite

APA Budak, V. Ö. (2021). Geçici Bilgi İhtiyacının Giderilme Sürecinde Kullanıcı Okuma Davranışlarının İncelenmesi . Türk Kütüphaneciliği , 35 (4) , 547-566 . DOI: 10.24146/tk.955630