Experimental performance evaluation of using multiset metrics in information retrieval problems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2014.23385

Keywords:

WEB-oriented systems, adaptive algorithms, information retrieval, performance criteria, multiset

Abstract

The problem of the comparative experimental performance evaluation using multiset metrics in information retrieval problems is considered in the paper. The main purpose of the studies is to prove on the basis of actual experimental data, the feasibility of using multisets, as a fundamentally new mathematical tool, in information retrieval problems. As a result of the preliminary statistical analysis of the available data set ‘’Anonymouswebdatafromwww.microsoft.com’’, specific features and problems of information search in the Internet space are identified. As the main performance indicator of information retrieval the “half-life of usefulness”, which predicts the user the usefulness of a list of recommended facilities, taking into account the user’s partial view of a list of recommendations, is used. The calculated values of the performance indicator of information retrieval are compared with the results of similar studies on the effectiveness of traditional, alternative methods of information retrieval. It is shown that the use of metric multisets in information retrieval problems improves the quality of information retrieval

Author Biography

Дмитрий Сергеевич Негурица, Kharkiv National University of Radio Electronics Lenina 14, Kharkov, Ukraine, 61166

Postgraduate

Department of Software Engineering

References

  1. Beck, J. Using the student model to control problem difficulty [Text] : proc. of the sixth inter. conf. / J. Beck, M. Stern, B. Woolf // User Modeling. − 1997. – UM97. − P. 277–288.
  2. Иванченко, Д. А. Системный анализ дистанционного обучения [Текст] / Д. А. Иванченко. − М.: Союз, 2005. − 192 с.
  3. Petchurai, O. Developing on-line information systems for searching digital media [Text] / O. Petchurai, S. Sombunsukho, C. Nanthawong, T. Atanan, S. Wechsopon, Y. Atanan. – WIETE Annual Conference on Engineering and Technology Education. Pattaya, 2011. – P. 43–45.
  4. Wood, D. Linking Enterprise Data [Text] / D. Wood. − Springer, 2010. − 317 p.
  5. Mourlas, C. Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies [Text] / C. Mourlas, P. Germanakos. − IGI Global Snippet, 2009. – 426 p.
  6. De Bra, P. AHAM: A Dexter-based Reference Model for Adaptive Hypermedia [Text] : proc.of the ACM conf. / P. De Bra, G. J. Houben, H. Wu // Hypertext and Hypermedia, Darmstadt, Germany, 1999. – P. 147–156.
  7. Fink, J. User Modeling Servers: Requirements, Design, and Evaluation [Text] / J. Fink. - IOS Press, 2004. – 189 p.
  8. Seffah, A. Multiple User Interfaces: Cross-Platform Applications and Context-Aware Interfaces [Text] / A. Seffah, John Javahery. – Wiley & Sons, 2005 – 414 p.
  9. Kules, В. User Modeling for Adaptive and Adaptable Software Systems [Electronic resource] / Available at: http://www.otal.umd.edu/UUGuide/jingwu/ usermodel.htm.
  10. Kobsa, А. Supporting User Interfaces for All Through User Modeling [Electronic resource] / Available at: http://www.ics.uci.edu/%7Ekobsa/papers/1995-HCI95-kobsa.pdf.
  11. Яковлев, Ю. С. О применении онтологии для построения модели пользователя информационных систем [Текст] / Ю. С. Яковлев, Л. И. Курзанцева // Комп’терні засоби, мережі та системи. − 2006. − № 5. − C. 109–116.
  12. Денинг, В. Диалоговая система «человек–ЭВМ». Адаптация к требованиям пользователя [Текст] / В. Денинг, Г. Эссинг, С. Маас. – М.: Мир, 1984. – 110 с.
  13. Скакун, С. В. Нейросетевая модель пользователей компьютерных систем [Текст] / С. В. Скакун, Н. Н. Куссуль // Кибернетика и вычислительная техника. – 2004. – Вып. 143. – С. 55–68.
  14. Петровский, А. Б. Пространства множеств и мультимножеств [Текст] /А. Б. Петровский. – М: Едиториал УРСС, 2003. – 248 с.
  15. Вовк, О. Л. Применение мультимножеств для ранжирования [Текст] / О. Л. Вовк // Вестник ХНТУ. Работы молодых ученых. – 2008. – № 1(30). – С. 498–502.
  16. Петровский, А. Б. Метрические пространства мультимножеств [Текст] / А. Б. Петровский // Доклады Академии наук. – 1995. - Т. 344, № 2. - C. 175–177.
  17. Anonymous Web data from www.microsoft.com / [Electronic resource] / Available at: http://kdd.ics.uci.edu/databases/msweb/msweb.data.html
  18. Breese, J. S. Empirical analysis of predictive algorithms for collaborative filtering [Text] : proc. of the fourteenth conf. / J. S. Breese, D. Heckerman, C. Kadie // Uncertainty in Artificial Intelligence (UAI-98). Morgan Kaufmann, San Francisco, 1998. − P. 43–52.
  19. Beck, J., Stern, M., Woolf, B. (1997). Using the student model to control problem difficulty. User Modeling, UM97, 277–288.
  20. Ivanchenko, D. A. (2005). System analysis of distance learning. Union, 192.
  21. Petchurai, O. Sombunsukho, S., Nanthawong, C., Atanan, T., Wechsopon, S., Atanan, Y. (2011). Developing on-line information systems for searching digital media. WIETE Annual Conference on Engineering and Technology Education. Pattaya, 43-45.
  22. Wood, D. (2010). Linking Enterprise Data, Springer, 317.
  23. Mourlas, C., Germanakos, P. (2009). Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies, IGI Global Snippet, 426.
  24. De Bra, P., Houben, G. J. , Wu, H. (1999). AHAM: A Dexterbased Reference Model for Adaptive Hypermedia, Proceedings of the ACM Conference on Hypertext and Hypermedia, Darmstadt, Germany, 147–156.
  25. Fink, J. (2004). User Modeling Servers: Requirements, Design, and Evaluation, IOS Press, 189.
  26. Seffah, A., Javahery, J. (2005). Multiple User Interfaces: Cross-Platform Applications and Context-Aware Interfaces, Wiley & Sons, 414.
  27. Kules, В. User Modeling for Adaptive and Adaptable Software Systems. Available at: http://www.otal.umd.edu/UUGuide/jingwu/ usermodel.htm.
  28. Kobsa, А. Supporting User Interfaces for All Through User Modeling. Available at: http://www.ics.uci.edu/%7Ekobsa/papers/1995-HCI95-kobsa.pdf.
  29. Yakovlev, Yu. S., Kurzantseva, L. I. (2006). On the application of ontologies to build a model user of information systems. Computer facilities, networks and systems, 5, 109–116.
  30. Dening, V., Essing, G., Maas, S. (1984). Interactive system “man-computer.” Adapting to the user’s requirements. Mir, 110.
  31. Skakun, S. V., Kussul, N. N. (2004). Neural network model of users of computer systems. Cybernetics and Computer Science, 143, 55–68.
  32. Petrovskiy, A. B. (2003). Space sets and multisets. Editorial URSS, 248.
  33. Vovk, O. L. (2008). Application multisets for ranking. Herald HNTU, 1 (30), 498–502.
  34. Petrovsky, A. B. (1995). Metric spaces multisets. Reports of the Academy of Sciences, № 2, 175-177.
  35. Anonymous. Available at: www.microsoft.com, http://kdd.ics.uci.edu/databases/msweb/msweb.data.html.
  36. Breese, J. S., Heckerman, D., Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Uncertainty in Artificial Intelligence (UAI-98). Morgan Kaufmann, San Francisco, 43–52.

Downloads

Published

2014-04-14

How to Cite

Негурица, Д. С. (2014). Experimental performance evaluation of using multiset metrics in information retrieval problems. Eastern-European Journal of Enterprise Technologies, 2(2(68), 38–43. https://doi.org/10.15587/1729-4061.2014.23385

Issue

Vol. 2 No. 2(68) (2014): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2014 Дмитрий Сергеевич Негурица

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.