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Bilgi Erişim Değerlendirmeleri ve Optimizasyonları İçin Modüler Bir Verimlilik Belirleme Formülü

Yıl 2023, Cilt: 7 Sayı: 1, 209 - 228, 02.01.2024

Veli Özcan Budak

https://doi.org/10.26650/acin.1198925

Öz

Verimlilik kavramı, Bilgi Erişim (BE) literatüründe temel olarak kullanıcılardan ziyade sistemlerin verimliliği ile ilişkilendirilmiştir. Kullanılabilirlik literatüründe ise bu kavram, bir kullanıcının bir görevi ne kadar sürede tamamladığına karşılık gelen kullanıcı tabanlı bir bakış açısıyla tanımlanır. Yine de, ortak amaç her iki literatür için de zamanı verimli kullanmaktır. Bu çalışma, BE literatüründeki etkinlik kavramını, kullanılabilirlik literatüründeki kullanıcı tabanlı etkinlik penceresinden incelemektedir. Bu çalışmada, kullanılabilirlik perspektifinden BE sistem değerlendirmelerine ve optimizasyonlarına odaklanarak farklı verimlilik göstergeleri oluşturmak için modüler bir verimlilik belirleme formülü (MEDEF) önerilmiştir. MEDEF, BE çalışmalarında hâlihazırda kullanılan etkililik metriklerine ve verimlilik göstergelerine dayalı bir verimlilik göstergesi üreticisi şeklinde düşünülebilir. Bu çalışma kapsamında, sekiz MEDEF tabanlı verimlilik göstergesi oluşturulmuş ve hâlihazırda BE çalışmalarında kullanılan birkaç temel verimlilik göstergesiyle karşılaştırılmıştır. Çalışmanın ilk amacı, MEDEF temelli göstergelerin ne kadar tutarlı olduğunu ve bu göstergelerin mevcut temel göstergelere göre daha başarılı/güvenilir olup olmadığını ortaya koymak iken, ikincisi, kullanılabilirlik açısından BE sistemlerinin değerlendirmelerinde verimlilik göstergelerinin kullanımına bir örnek oluşturmaktır. Bir aylık etkileşimli kullanıcı davranışlarından elde edilen genel bulgular, MEDEF tabanlı göstergelerin temel göstergelerden daha iyi performans gösterdiğini ve temel göstergelerdeki yansımaları daha da güçlendirdiğini göstermiştir. MEDEF‘in potansiyeline ilişkin çeşitli kullanım senaryoları da çalışma kapsamında paylaşılmakta ve tartışılmaktadır.

Anahtar Kelimeler

İnsan-Bilgisayar Etkileşimi Verimlilik Göstergesi Oturum Terk Etme Etkileşimli Bilgi Erişim

Kaynakça

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A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations

Yıl 2023, Cilt: 7 Sayı: 1, 209 - 228, 02.01.2024

Veli Özcan Budak

https://doi.org/10.26650/acin.1198925

Öz

The notion of efficiency has typically been associated with the efficiency of systems rather than users in Information Retrieval (IR) literature. In the usability literature, on the other hand, this notion is defined from a user-based perspective, corresponding to how long a user accomplishes a task. Despite this, the common aim for both has to do with the efficient use of time. This study examines the efficiency notion in the IR literature from a user-based efficiency window in the usability literature. In the present study, a modular efficiency determination formula (MEDEF) to create different efficiency indicators by focusing on IR system evaluations and optimizations from the usability perspective is proposed. The MEDEF can be thought of as an efficiency indicator creator based on both effectiveness metrics and efficiency indicators already used in IR studies. In the scope of this study, eight MEDEF-based efficiency indicators were created and compared to several baseline efficiency indicators already used in IR studies. While the study’s first aim is to reveal how consistent the MEDEF-based indicators are and whether these indicators are more successful/reliable than the baselines, the second is to set an example of the usage of efficiency indicators in evaluations of IR systems from a usability perspective. General findings from interactive user behaviour for one month show that the MEDEF-based indicators outperform the baseline indicators and further strengthen the reflections in the baseline indicators. Several usage scenarios regarding the potential of the MEDEF are also shared and discussed in the scope of the study.

Anahtar Kelimeler

Human-Computer Interaction Efficiency Indicator Session Abandonment Interactive Information Retrieval

Kaynakça

  • Agichtein, E., Brill, E., & Dumais, S. (2006). Improving web search ranking by incorporating user behavior information. Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006, 19-26. New York, New York, USA: ACM Press. https://doi.org/10.1145/1148170.1148177 google scholar
  • Agichtein, E., Brill, E., Dumais, S., & Ragno, R. (2006). Learning user interaction models for predicting web search result preferences. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’06, 3. New York, New York, USA: ACM Press. https://doi.org/10.1145/1148170.1148175 google scholar
  • Alhabashneh, O., Iqbal, R., Doctor, F., & James, A. (2017). Fuzzy rule based profiling approach for enterprise information seeking and retrieval. Information Sciences, 394-395, 18-37. https://doi.org/10.1016/J.INS.2016.12.040 google scholar
  • Arguello, J. (2014). Predicting search task difficulty. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 8416 LNCS (pp. 88-99). https://doi.org/10.1007/978-3-319-06028-6_8 google scholar
  • Arkhipova, O., Grauer, L., Kuralenok, I., & Serdyukov, P. (2015). Search Engine Evaluation based on Search Engine Switching Prediction. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 723-726. New York, NY, USA: ACM. https://doi.org/10.1145/2766462.2767786 google scholar
  • Aula, A., Khan, R. M., & Guan, Z. (2010). How does search behavior change as search becomes more difficult? Proceedings of the 28th International Conference on Human Factors in Computing Systems - CHI ’10, 35. New York, New York, USA: ACM Press. https://doi.org/10.1145/1753326.1753333 google scholar
  • Balakrishnan, V., & Zhang, X. (2014). Implicit user behaviours to improve post-retrieval document relevancy. Computers in Human Behavior, 33, 104-112. https://doi.org/10.1016/J.CHB.2014.01.001 google scholar
  • Beierle, F., Aizawa, A., Collins, A., & Beel, J. (2020). Choice overload and recommendation effectiveness in related-article recommendations. International Journal on Digital Libraries, 21(3), 231-246. https://doi.org/10.1007/s00799-019-00270-7 google scholar
  • Belkin, N. J., Kelly, D. F., Kim, G., Kim, J. Y., Lee, H., Muresan, G., Tang, M. C., Yuan, X., Cool, C. (2003). Query Length in Interactive Information Retrieval. SIGIR Forum (ACM Special Interest Group on Information Retrieval), (SPEC. ISS.), 205-212. New York, New York, USA: ACM Press. https://doi.org/10.1145/860472.860474 google scholar
  • Borisov, A., Markov, I., de Rijke, M., & Serdyukov, P. (2016). A Context-aware Time Model for Web Search. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 205-214. New York, NY, USA: ACM. https://doi.org/10.1145/2911451.2911504 google scholar
  • Buckley, C., & Voorhees, E. M. (2004). Retrieval evaluation with incomplete information. Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval - SIGIR ’04, 25. New York, New York, USA: ACM Press. https://doi.org/10.1145/1008992.1009000 google scholar
  • Büttcher, S., Clarke, C. L. A., & Cormack, G. V. (2010). Information Retrieval: Implementing and Evaluating Search Engines. The MIT Press. google scholar
  • Cen, R., Liu, Y., Zhang, M., Ru, L., & Ma, S. (2009). Study on the click context of web search users for reliability analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 5839 LNCS (pp. 397-408). https://doi.org/10.1007/978-3-642-04769-5_35 google scholar
  • Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009). Expected reciprocal rank for graded relevance. Proceeding of the 18th ACM Conference on Information and Knowledge Management - CIKM ’09, 621. New York, New York, USA: ACM Press. https://doi.org/10.1145/1645953.1646033 google scholar
  • Claypool, M., Brown, D., Le, P., & Waseda, M. (2001). Inferring user interest. IEEE Internet Computing, 5(6), 32-39. https://doi.org/10.1109/4236.968829 google scholar
  • Colace, F., De Santo, M., Greco, L., & Napoletano, P. (2015). Improving relevance feedback-based query expansion by the use of a weighted word pairs approach. Journal of the Association for Information Science and Technology, 66(11), 2223-2234. https://doi.org/10.1002/asi.23331 google scholar
  • Croft, B., Metzler, D., & Strohman, T. (2009). Search Engines: Information Retrieval in Practice (1st ed.). Boston: Pearson. google scholar
  • Diriye, A., White, R., Buscher, G., & Dumais, S. (2012). Leaving so soon?: understanding and predicting web search abandonment rationales. Proceedings of the 21st ACM International Conference on Information and Knowledge Management - CIKM ’12, 1025. New York, New York, USA: ACM Press. https://doi.org/10.1145/2396761.2398399 google scholar
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830. Retrieved from http://scikit-learn.sourceforge.net. google scholar
  • Fox, S., Karnawat, K., Mydland, M., Dumais, S., & White, T. (2005). Evaluating implicit measures to improve Web search. ACM Transactions on Information Systems, 23(2), 147-168. https://doi.org/10.1145/1059981.1059982 google scholar
  • Frokjrer, E., Hertzum, M., & Hornbrek, K. (2000). Measuring usability: are effectiveness, efficiency, and satisfaction really correlated? Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI ’00, 345-352. New York, New York, USA: ACM Press. https://doi. org/10.1145/332040.332455 google scholar
  • Hassan, A. (2012). A semi-supervised approach to modeling web search satisfaction. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’12, 275. New York, New York, USA: ACM Press. https://doi.org/10.1145/2348283.2348323 google scholar
  • Hassan, A., Song, Y., & He, L. (2011). A task level metric for measuring web search satisfaction and its application on improving relevance estimation. Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM ’11, 125. New York, New York, USA: ACM Press. https://doi.org/10.1145/2063576.2063599 google scholar
  • Hassan, A., White, R. W., Dumais, S. T., & Wang, Y.-M. (2014). Struggling or exploring? Disambiguating Long Search Sessions. Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 53-62. New York, NY, USA: ACM. https://doi.org/10.1145/2556195.2556221 google scholar
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Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Veli Özcan Budak 0000-0002-0960-0542

Yayımlanma Tarihi 2 Ocak 2024
Gönderilme Tarihi 3 Kasım 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 1

Kaynak Göster

APA Budak, V. Ö. (2024). A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations. Acta Infologica, 7(1), 209-228. https://doi.org/10.26650/acin.1198925
AMA Budak VÖ. A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations. ACIN. Ocak 2024;7(1):209-228. doi:10.26650/acin.1198925
Chicago Budak, Veli Özcan. “A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations”. Acta Infologica 7, sy. 1 (Ocak 2024): 209-28. https://doi.org/10.26650/acin.1198925.
EndNote Budak VÖ (01 Ocak 2024) A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations. Acta Infologica 7 1 209–228.
IEEE V. Ö. Budak, “A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations”, ACIN, c. 7, sy. 1, ss. 209–228, 2024, doi: 10.26650/acin.1198925.
ISNAD Budak, Veli Özcan. “A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations”. Acta Infologica 7/1 (Ocak 2024), 209-228. https://doi.org/10.26650/acin.1198925.
JAMA Budak VÖ. A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations. ACIN. 2024;7:209–228.
MLA Budak, Veli Özcan. “A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations”. Acta Infologica, c. 7, sy. 1, 2024, ss. 209-28, doi:10.26650/acin.1198925.
Vancouver Budak VÖ. A Modular Efficiency Determination Formula for Information Retrieval Evaluations and Optimizations. ACIN. 2024;7(1):209-28.
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