Abstract
Metasearch engines are the web services that receive user queries and dispatch them to multiple crawler based search engines. After this, they collect the returned search results, reorder them and present the reordered list to the end user. To combine the results from different search engines, a metasearch engine may use different rank aggregation techniques to aggregate the various rankings of the search results to generate an overall ranking. If different rank aggregation techniques are used to collate search results, the results of metasearching for the same query may vary for the same set of participating search engines. In this paper, we discuss a metasearching technique that utilizes neural network based rank aggregation. Here, we formulate the rank aggregation problem as a function approximation problem. As the multilayer perceptrons are considered universal approximators, we use a multilayer perceptron for rank aggregation. We compare the performance of the neural network based method with four other methods namely rough set based method, modified rough set based method, Borda’s method and a Markov Chain based method (MC2) using three independent evaluators. Experimentally, we find that the neural network based method performs better than each of these four methods.
Similar content being viewed by others
References
Aslam JA, Montague M (2001) Models for metasearch. In: Croft WB, Harper DJ, Kraft DH, Zobel J (eds) Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, pp 276–284
Montague M, Aslam JA (2002) Condorcet fusion for improved retrieval. In: Proceedings of the 11th International Conference on Information and Knowledge Management, pp 538–548
Vogt CC, Cottrell GW (1999) Fusion via a linear combination of scores. Inf Retrieval 1(3):151–173
Dwork C, Kumar R, Naor M, Sivakumar D (2001) Rank aggregation methods for the web. In: Proceedings of the tenth international conference on World Wide Web, pp 613–622
Renda ME, Straccia U (2003) Web metasearch: Rank vs. score based rank aggregation methods. In: Proceedings of the 18th Annual Symposium on Applied Computing, pp 841–846
Ali R, Sufyan Beg MM (2007) A learning algorithm for metasearching using rough set theory. In: Proceedings of the 10th International Conference on Computer and Information Technology (ICCIT 2007), IEEE Press, Dhaka, Bangladesh, pp 361–366
Ali R, Saxena A, Gupta R, Sufyan Beg MM (2011) Myriad-a novel user feedback based metasearch engine. In: Proceedings of the 2011 International Conference on Control, Robotics and Cybernetics (ICCRC 2011) vol 1, New Delhi, India, pp 163-167
Borda JC (1781) Memoire sur les election au scrutiny. Histoire de l’Academie Royale des Sciences
Cheng CT, Wang WC, Xu DM, Chau KW (2008) Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour Manage 22(7):895–909
Jia W, Ling B, Chau KW, Heutte L (2008) Palmprint identification using restricted fusion. Appl Math Comput 205(2):927–934
Xie JX, Cheng CT, Chau KW, Pei YZ (2006) A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity. Int J Environ Pollut 28(3–4):364–381
Lin JY, Cheng CT, Chau KW (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51(4):599–612
Wu CL, Chau KW, Li YS (2009) Predicting monthly stream flow using data-driven models coupled with data-preprocessing techniques. Water Resour Res 45:8432. doi:10.1029/2007WR006737
Zhang J, Chau KW (2009) Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. J Univers Comput Sci 15(4):840–858
Beg MMS, Ahmad N (2003) Soft computing techniques for rank aggregation on the World Wide Web. WorldW Int J 6(1):5–22
Ahmad N, Beg MMS (2002) Improved methods for rank aggregation on the World Wide Web. In: Proceedings of the International Conference on Knowledge Based Computer Systems (KBCS 2002), Mumbai, India, Dec 19-21, pp 193–202
Beg MMS, Ahmad N (2002) Improved shimura technique for rank aggregation on the World Wide Web. In: Proceedings of the 5th International Conference on Information Technology (CIT 2002), Bhubaneswar, India, Dec, 21–24
Beg MMS (2004) Parallel rank aggregation for the World Wide Web. In: Proceedings of the International Conference on Intelligent Sensing and Information Processing (ICISIP – 2004), IEEE Press, Chennai, India, Jan 4–7, pp 385–390
Beg MMS, Ahmad N (2002) Genetic algorithm based rank aggregation for the Web. In: Proceedings of the 6th International Conference on Computer Science and Informatics—a track at the 6th Joint Conference on Information Sciences (JCIS 2002), Durham, NC, USA, Mar 8–13, pp 329–333
Ahmad N, Beg MMS (2002) Fuzzy logic based rank aggregation methods for the World Wide Web. In: Proceedings of the International Conference on Artificial Intelligence in Engineering and Technology (ICAIET 2002), Malaysia, June 17–18, pp 363–368
Ali R, Beg MMS (2009) A comparative study of rough set and fuzzy set based rank aggregation techniques for the Web. Int J Info Process 3(1):78–91 (ISSN 0973–8215)
Ali R, Beg MMS (2007) Rough set based rank aggregation for the Web. In: Proceedings of the 3rd Indian International Conference on Artificial Intelligence (IICAI-07), Pune, India, Dec 17–19, pp 683–698
Ali R, Beg MMS (2008) User feedback based metasearching using rough set theory. Int J Fuzzy Syst Rough Syst (IJFSRS) 1(2):45–56
Ali R, Beg MMS (2008) User feedback based metasearching using rough set theory. In: Proceedings of the 2008 International Conference on Information and Knowledge Engineering (IKE’08)—a track at the 2008 World Congress in Computer Science, Computer Engineering and Applied Computing (WORLDCOMP’08), Las Vegas, USA, July 14–17, pp 489–495
Ali R, Sufyan Beg MM (2009) Modified rough set based aggregation for effective evaluation of Web search systems. In: Proceedings of the 28th North American Fuzzy Information Processing Society Annual Conference (NAFIPS2009), IEEE Press, Cincinnati, Ohio, U.S.A., June
Haykin S (1999) Neural networks. Prentice-Hall International Inc., 1999
Barakat M, Lefebvre D, Khalil M, Druaux F, Mustapha O (2013) Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues. Int J Mach Learn Cybern 4(3):217–233
Zheng Huiru, Wang Haiying (2012) Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations. Int J Mach Learn Cybern 3(3):173–182
Wang Xizhao, Dong Chun-Ru, Fan Tie-Gang (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70(13–15):2581–2587
Tsang Eric, Wang Xizhao, Yeung Daniel (2000) Improving learning accuracy of fuzzy decision trees by hybrid neural networks. IEEE Trans Fuzzy Syst 8(5):601–614
Fausett L (1994) Fundamental of neural networks: architectures, algorithms and application. Prentice-Hall, New Jersey
Freeman JA, Skapura DM (2005) Neural networks: algorithms, application and programming techniques. Pearson-Education Private Ltd., 2005
Beg MMS (2002) On measurement and enhancement of Web search quality. Ph.D. thesis submitted to the Department of Electrical Engineering, I. I. T. Delhi, India
Beg MMS, Ahmad N (2007) Web search enhancement by mining user actions. Int J Inform Sci 177(23):5203–5218 (Elsevier Science)
Ali R, Beg MMS (2007) A framework for evaluating web search systems. WSEAS Trans Syst 6(2):257–264
Ali R, Beg MMS (2009) Automatic performance evaluation of web search systems using rough set based rank aggregation. In: Proceedings of the IEEE Workshop on Recent Trends in Human Computer Interaction, Springer, ISBN No. 978-81-8489-203-1, Allahabad, India, Jan 19–21, pp 344–358
Ali R, Beg MMS (2006) A comprehensive model for web search evaluation. In: Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing (CSECS ‘06), Dallas, USA, Nov 1–3, pp 159–164
Ali R, Beg MMS (2011) An overview of web search evaluation methods. Comput Electr Eng Int J 37(6):835–848
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ali, R., Naim, I. User feedback based metasearching using neural network. Int. J. Mach. Learn. & Cyber. 6, 265–275 (2015). https://doi.org/10.1007/s13042-013-0212-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-013-0212-2