Learning Ranking Model Adaption in the vector of the Domain- Specific Search

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-21 Number-3
Year of Publication : 2015
Authors : Deepakkishore Bokam, R.Sailaja

MLA

Deepakkishore Bokam, R.Sailaja "Learning Ranking Model Adaption in the vector of the Domain- Specific Search". International Journal of Computer Trends and Technology (IJCTT) V21(3):1116-120, March 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Technology and its significance always takes mile stone base for achievement of the goal, but if we consider the approach of the usual methodology involved in the traditional software design, it’s really an amazing and strong base to next level of concept. In this paper we consider the concept of the vertical domain and the mess up for the same in order to search keyword of Meta tag of Meta description. In the model of the domain culture, vertical domain culture provides service to the utmost best level. In this paper, we implemented the concept of the vector model of domain adoption and learning ranking methodology to give the best to the user of the domain and its related, in order to resolve the cross domain issue. In the security mechanism where the ranking is important we have implemented the key word with unique key and map paring of Hadoop big data analytics. In the context it gives the effectiveness, time forward and the most robust and best of the all classical ranking methodology.

References
[1] M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold Regularization:A Geometric Framework for Learning from Labeled and Unlabeled Examples,” J. Machine Learning Research, vol. 7, pp. 2399-2434, Nov. 2006.
[2] J. Blitzer, R. Mcdonald, and F. Pereira, “Domain Adaptation with Structural Correspondence Learning,” Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP ’06), pp. 120-128, July 2006.
[3] C.J.C. Burges, R. Ragno, and Q.V. Le, “Learning to Rank with Nonsmooth Cost Functions,” Proc. Advances in Neural Information Processing Systems (NIPS ’06), pp. 193-200, 2006.
[4] C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, “Learning to Rank Using Gradient Descent,” Proc. 22th Int’l Conf. Machine Learning (ICML ’05), 2005.
[5] Z. Cao and T. Yan Liu, “Learning to Rank: From Pairwise Approach to Listwise Approach,” Proc. 24th Int’l Conf. Machine Learning (ICML ’07), pp. 129-136, 2007.
[6] J. Cui, F. Wen, and X. Tang, “Real Time Google and Live Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, pp. 729-732, 2008.
[7] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, “Boosting for Transfer Learning,” Proc. 24th Int’l Conf. Machine Learning (ICML ’07), pp. 193-200, 2007.
[8] H. Daume III and D. Marcu, “Domain Adaptation for Statistical Classifiers,” J. Artificial Intelligence Research, vol. 26, pp. 101-126, 2006.
[9] Y. Freund, R. Iyer, R.E. Schapire, Y. Singer, and G. Dietterich, “An Efficient Boosting Algorithm for Combining Preferences,” J. Machine Learning Research, vol. 4, pp. 933-969, 2003.
[10] B. Geng, L. Yang, C. Xu, and X.-S. Hua, “Ranking Model Adaptation for Domain-Specific Search,” Proc. 18th ACM Conf. Information and Knowledge Management (CIKM ’09), pp. 197-206, 2009.
[11] F. Girosi, M. Jones, and T. Poggio, “Regularization Theory and Neural Networks Architectures,” Neural Computation, vol. 7, pp. 219-269, 1995.
[12] R. Herbrich, T. Graepel, and K. Obermayer, “Large Margin Rank Boundaries for Ordinal Regression,” Advances in Large Margin Classifiers, pp. 115-132, MIT Press, 2000.
[13] K. Ja¨rvelin and J. Keka¨la¨inen, “Ir Evaluation Methods for Retrieving Highly Relevant Documents,” Proc. 23rd Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’00), pp. 41- 48, 2000.
[14] T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proc. Eighth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’02), pp. 133-142, 2002.
[15] T. Joachims, “Training Linear Svms in Linear Time,” Proc. 12th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’06), pp. 217-226, 2006.
[16] M.G. Kendall, “A New Measure of Rank Correlation,” Biometrika, vol. 30, nos. 1/2, pp. 81-93, June 1938.
[17] J.M. Kleinberg, S.R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, “The Web as a Graph: Measurements, Models and Methods,” Proc. Int’l Conf. Combinatorics and Computing, pp. 1-18, 1999.
[18] R. Klinkenberg and T. Joachims, “Detecting Concept Drift with Support Vector Machines,” Proc. 17th Int’l Conf. Machine Learning (ICML ’00), pp. 487-494, 2000.

Keywords
Information retrieval, support vector machines, learning to rank, domain adaptation.