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

International Journal of Computer Trends and Technology (IJCTT)  
© 2015 by IJCTT Journal  
Volume21 Number3 

Year of Publication : 2015  
Authors : Deepakkishore Bokam, R.Sailaja 
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):1116120, March 2015. ISSN:22312803. 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.
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Keywords
Information retrieval, support vector machines, learning to rank, domain adaptation.