CFP last date
22 April 2024
Reseach Article

A Technique for Web Page Ranking by Applying Reinforcement Learning

by Vivek Deshmukh, S. S. Barve
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 7
Year of Publication: 2016
Authors: Vivek Deshmukh, S. S. Barve
10.5120/ijca2016911237

Vivek Deshmukh, S. S. Barve . A Technique for Web Page Ranking by Applying Reinforcement Learning. International Journal of Computer Applications. 155, 7 ( Dec 2016), 1-4. DOI=10.5120/ijca2016911237

@article{ 10.5120/ijca2016911237,
author = { Vivek Deshmukh, S. S. Barve },
title = { A Technique for Web Page Ranking by Applying Reinforcement Learning },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 7 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume155/number7/26614-2016911237/ },
doi = { 10.5120/ijca2016911237 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:00:36.793326+05:30
%A Vivek Deshmukh
%A S. S. Barve
%T A Technique for Web Page Ranking by Applying Reinforcement Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 7
%P 1-4
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ranking of site pages is for showing important web pages to client inquiry it is a one of the essential issue in any web search index tool. Today’s need is to get significant data to client inquiry. Importance of web pages is depending on interest of users. There are two ranking algorithm is utilized to demonstrate the current raking framework. One is page rank and another is BM25 calculation. Reinforcement learning strategy learns from every connection with dynamic environment. In this paper Reinforcement learning (RL) ranking algorithm is proposed. In this learner is specialist who learns through interactopm with dynamic environment and gets reward of an activity performed. Every site page is considered as a state and fundamental point is to discover score of website page. Score of website pages is identified with number of out connections from current website page .Rank scores in RL rank as considered in recursive way. Along these lines we can enhance outcomes with help of RL method in ranking algorithm.

References
  1. Henzinger, M. R., Motwani, R., & Silverstein, C. (2002). Challenges in web search engines. In ACM SIGIR Forum, vol. 36, no. 2, pp. 11-22.
  2. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks, science, vol. 286, no. 5439, pp. 509-512.
  3. Agichtein, E., Brill, E., & Dumais, S. (2006). Improving web search ranking by incorporating user behavior information. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 19-26). ACM, 2006.
  4. Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., & Gay, G. (2007). Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Transactions on Information Systems (TOIS), vol. 25, no. 2, pp. 7.
  5. Granka, L. A., Joachims, T., & Gay, G. (2004, July). Eye-tracking analysis of user behavior in WWW search. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 478-479). ACM, 2004.
  6. Xue, G. R., Zeng, H. J., Chen, Z., Yu, Y., Ma, W. Y., Xi, W., & Fan, W. (2004, November). Optimizing web search using web click-through data. In Proceedings of the thirteenth ACM international conference on Information and knowledge management (pp. 118-126). ACM, 2004.
  7. Qin, T., Liu, T. Y., Xu, J., & Li, H. (2010). LETOR: A benchmark collection for research on learning to rank for information retrieval. Information Retrieval, vol. 13, no. 4, pp. 346-374.
  8. Li, L., & Lin, H. T. (2006). Ordinal regression by extended binary classification. In Advances in neural information processing systems (pp. 865-872), 2006.
  9. Li, P., Wu, Q., & Burges, C. J. (2007). Mcrank: Learning to rank using multiple classification and gradient boosting. In Advances in neural information processing systems (pp. 897-904), 2007.
  10. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web, Technical Report. Stanford InfoLab, (pp.1-17, 1999, Available: http://ilpubs.stanford.edu:8090/422/.
  11. Robertson, S. E., & Walker, S. (1994). Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 232-241). Springer-Verlag New York, 1994.
  12. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, vol. 24, no. 5, pp. 513-523.
  13. Zareh Bidoki, A. M., Ghodsnia, P., Yazdani, N., & Oroumchian, F. (2010). A3CRank: An adaptive ranking method based on connectivity, content and click-through data. Information processing & management, vol. 46, no. 2, pp. 159-169.
  14. Zareh Bidoki, A. M., & Yazdani, N. (2008). DistanceRank: An intelligent ranking algorithm for web pages. Information Processing & Management, vol. 44, no. 2, pp. 877-892.
  15. Derhami, V., Khodadadian, E., Ghasemzadeh, M., & Bidoki, A. M. Z. (2013). Applying reinforcement learning for web pages ranking algorithms. Applied Soft Computing, vol. 13, no. 4, pp. 1686-1692.
  16. Khodadadian, E., Ghasemzadeh, M., Derhami, V., & Mirsoleimani, S. A. (2012). A novel ranking algorithm based on Reinforcement Learning. In Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on (pp. 546-551). IEEE, 2012.
  17. Derhami, V., Paksima, J., & Khajeh, H. (2014). RLRAUC: Reinforcement learning based ranking algorithm using user clicks. In Computer and Knowledge Engineering (ICCKE), 2014 4th International eConference on (pp. 29-34). IEEE, 2014.
  18. Anari, Z., Meybodi, M. R., & Anari, B. (2009). Web page ranking based on fuzzy and learning automata. In Proceedings of the International Conference on Management of Emergent Digital EcoSystems (p. 24). ACM, 2009.
  19. Forsati, R., Meybodi, M. R., & Mahdavi, M. (2007). Web page personalization based on distributed learning automata. Proc. IKT, Ferdowsi University of Mashad, Mashad, Iran, 2007.
  20. Yarahmadi, T., Torkestani, J. A., & Fatemeh, Z. (2012). A new method based on distributed learning automata for page ranking in web. International Journal of Physical Sciences, vol. 7, no. 13, pp. 2066-2075.
  21. Saati, S., & Meybodi, M. R. (2006, May). Document Ranking using Distributed Learning Automata. In Proceedings of 11th Annual CSI Computer Conference of Iran, Fundamental Science Research Center (IPM), Computer Science Research Lab, Tehran, Iran, 2006.
  22. Phophalia, A. (2011, December). A survey on learning to rank (letor) approaches in information retrieval. In Engineering (NUiCONE), 2011 Nirma University International Conference on (pp. 1-6). IEEE, 2011
Index Terms

Computer Science
Information Sciences

Keywords

Ranking Search engine Agent Value function Reinforcement Learning Artificial intelligence