CFP last date
20 January 2025
Reseach Article

Neural Network based Approach for Predicting user Satisfaction with Search Engine

by Sunita Yadav, Om Prakash Sangwan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 18 - Number 5
Year of Publication: 2011
Authors: Sunita Yadav, Om Prakash Sangwan
10.5120/2281-2953

Sunita Yadav, Om Prakash Sangwan . Neural Network based Approach for Predicting user Satisfaction with Search Engine. International Journal of Computer Applications. 18, 5 ( March 2011), 16-21. DOI=10.5120/2281-2953

@article{ 10.5120/2281-2953,
author = { Sunita Yadav, Om Prakash Sangwan },
title = { Neural Network based Approach for Predicting user Satisfaction with Search Engine },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 18 },
number = { 5 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume18/number5/2281-2953/ },
doi = { 10.5120/2281-2953 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:05:30.480481+05:30
%A Sunita Yadav
%A Om Prakash Sangwan
%T Neural Network based Approach for Predicting user Satisfaction with Search Engine
%J International Journal of Computer Applications
%@ 0975-8887
%V 18
%N 5
%P 16-21
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Success of a search engine is measured by the satisfaction of its users. Finding user expectation can be a better step for improved user satisfaction. In this paper we have proposed a neural network based approach for predicting user satisfaction with search engine. Our work is divided in two parts. Part I investigates user expectations towards search engine for their information need. In Part II we proposed an Artificial Neural Network (ANN) model for predicting User Satisfaction. In our work we have analyzed the major factors affecting user satisfaction with search engine and find out the importance /priority value of these factors based on a survey conducted on 100 search engine users of different profiles with 5-10 years of experience using search engines for their information needs like study material, entertainment, research, day to day problem solution etc. In the present work we have identified four major factors namely up-to-date information, search result relevancy, response time and reliability, contributing to the user satisfaction and developed an ANN model which predicts satisfaction results with a reasonable degree of accuracy.

References
  1. Ali Selamat and Sigeru Omatu,”Neural Networks for Web Page Classification Based on Augmented PCA”, Proceedings of the International Joint Conference on Neural Networks, 2003, Volume 3, pages 1792 – 1797
  2. Anagnostopoulos, I. Et al., “Classifying Web pages employing a Probabilistic Neural Network”, Proceedings: Software, v151, n3, June 2004, page 139-150.
  3. Franco Scarselli, Sweah Liang Yong, Markus Hagenbuchner, Ah Chung Tsoi, “Adaptive page ranking with neural networks”, Special interest tracks and posters of the 14th international conference on World Wide Web, May 2005, pages 936-937.
  4. Giovanni Pilato, Salvatore Vitabile, Giorgia Vassallo, Vincenzo Conti, Filippo Sorbello, “ A Neural Multi-Agent Based System for Smart Html Pages Retrieval,” International Conference on Intelligent Agent Technology (IAT'03), October 2003, pages 233.
  5. Haykin, S. (2003), Neural Networks, A Comprehensive Foundation, Prentice Hall India.
  6. Hudson, D. L., Cohen, M. E., 2003. Neural Networks & Artificial Intelligence for Biomedical Engineering, Prentice Hall of India.
  7. H. Chen, M. Chau, and D. Zeng, “CI Spider: A tool for competitive intelligence on the Web,” Decis. Support Syst., vol. 34, no. 1, pp. 1–17, 2002
  8. Jiu-zhen Liang, “Chinese Web page classification based on self organizing mapping neural networks,” Computational Intelligence and Multimedia Applications, ICCIMA, Sep 2003, pages 96-101.
  9. Kartik Menon and Cihan H. Dagli, "Web Personalization using Neuro-Fuzzy Clustering Algorithms," NAFIPS 2003 22nd International Conference of the North American, IEEE, July 2003, pages 525-529.
  10. Ko-Kang Chu,Maiga Chang and Yen-Teh Hsia,” A Hybrid Training Mechanism for Applying Neural Networks to Web-based Applications”, International Conference on Systems, Man and Cybernetics, 2004, pages 3543-3547.
  11. Lee, P.Y.; Hui, S.C.; Fong, "Neural Networks for Web Content Filtering", Intelligent Systems, IEEE Volume 17, Issue 5, Sep/Oct 2002, pages 48 – 57.
  12. Mayrhauser A., Anderson C. and Mraz R. (1995), Using a Neural Network to Predict Test Case Effectiveness, Proceedings of IEEE Aerospace Applications Conference, Snowmass, CO, pp:77-91.
  13. Mukherjee, A. and Deshpande , J. M., 1995. Neural Network Based Expert Systems for Structural Design, Computers and Structures, Vol. 54, Iss. 3, pp 367-375.
  14. Ma. Heng "Fast Blocking of Undesirable Web pages on Client PC by discriminating URL using Neural Networks," Expert Systems With Applications, 2007.
  15. O. Gogan and S. C. Buraga, “The use of neural networks for structural search on Web,” presented at the Int. Symp. Syst. Theory—SINTES10, Craiova, Romania, May 25–26, 2000.
  16. Olfa Nasraoui, Mrudula Pavuluri, “Accurate web recommendations based on profile-specific url- predictor neural networks”, Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters WWW Alt. '04, May 2004, pages 300-301.
  17. P. Batista andM. J. Silva, “Web access mining from an on-line newspaper logs,” presented at the 12th Int. Meet. Euro Work. Group Decis. Support Syst. (EWG-DSS 2001), Cascais, Portugal, May 2001.
  18. Seda Özmutlu, Fatih Çavdur. “Neural network applications for automatic new topic identification.” Online Information Review, 2005, pages 34-53.
  19. Selamat A., & Omatu S. (2003).“Web Page Feature Selection and Classification Using Neural Networks”, Information Sciences.
  20. Sunita Yadav, Omprakash Sangwan,” Standardization Specification of Factors Affecting User Satisfaction with Search Engine”, Proceeding of National Conference on Advanced Computing and Communication Technology- ACCT2010, June 2010 pages909-914.
  21. Vlajic, N.; Card, H.C., "An Adaptive Neural Network Approach to Hypertext Clustering", Neural Networks, 1999. IJCNN'99. International Joint Conference, Volume 6, Jul 1999 pages 3722-3726.
  22. Widrow, B., Rumelhart, D. E., and Lehr, M. A., I 994. Neural networks: Applications in industry, business, and science. Communications of the ACM, Vol. 37, pp: 93-105.
  23. Xing Zhu, Shen Huang, Yong Yu, “Web technologies and applications: Recognizing the relations between Web pages using artificial neural network”, Proceedings of the 2003 ACM symposium on Applied computing SAC '03, ACM Press, 2003, pages1217-1221.
  24. Y. Jin and B. Sendhoff, “Knowledge incorporation into neural networks from fuzzy rules,” Neural Process. Lett., vol. 10, pp. 231–242, 1999.
  25. Ying Xie, Dheerendranath Mundluru, Vijay V. Raghavan,”Incorporating Agent Based Neural Network Model for Adaptive Meta-Search”, Proceedings of the 43rd annual Southeast regional conference, Volume 1, Mar 2005, pages 53-58.
Index Terms

Computer Science
Information Sciences

Keywords

Search Engine up-to-date information search result relevancy Response Time Weight Value Reliability Freshness ANN and user satisfaction engines