Efficient Robust Interactive Personalized Mobile Search Engine

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-19 Number-1
Year of Publication : 2015
Authors : Mitikela. Guruswami , T.Sunitha


Mitikela. Guruswami , T.Sunitha "Efficient Robust Interactive Personalized Mobile Search Engine". International Journal of Computer Trends and Technology (IJCTT) V19(1):30-33, Jan 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Technology and its Service Based cluster mechanism plays the most important role in the building the Technology in better and smarter way to implement the best of technological advancement. In the Context of recent era where Mobile plays like a pc or we can call these days mobile more efficient and robust to provide the service better than a PC. In this Paper, we try to implement the concept of the interactive and responsive search interface to get the ontology based on the personalization. In the location based search engine where click through rate taken as the user profiling based on which the metadata search mechanism would like to take the smart call. In the context of the user profiling we have implemented the concept of the most visited terminology, the web pages and the based on the profile data like age, gender etc. Hence, In order to achieve the best efficient way of including the Meta data based tag search where key plays the approach for getting the value on the chain of the Meta tag and Meta descriptor based ontological methodology.

[1]Appendix, http://www.cse.ust.hk/faculty/dlee/tkde-pmse/appendix.pdf, 2012.
[2] Nat’l geospatial, http://earth-info.nga.mil/, 2012.
[3] svmlight, http://svmlight.joachims.org/, 2012.
[4] World gazetteer, http://www.world-gazetteer.com/, 2012.LEUNG ET AL.: PMSE: A PERSONALIZED MOBILE SEARCH ENGINE 833
[5] E. Agichtein, E. Brill, and S. Dumais, “Improving Web Search Ranking by Incorporating User Behavior Information,” Proc. 29th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2006.
[6] E. Agichtein, E. Brill, S. Dumais, and R. Ragno, “Learning User Interaction Models for Predicting Web Search Result Preferences,” Proc. Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2006.
[7] Y.-Y. Chen, T. Suel, and A. Markowetz, “Efficient Query Processing in Geographic Web Search Engines,” Proc. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2006.
[8] K.W. Church, W. Gale, P. Hanks, and D. Hindle, “Using Statistics in Lexical Analysis,” Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon, Psychology Press, 1991.
[9] Q. Gan, J. Attenberg, A. Markowetz, and T. Suel, “Analysis of Geographic Queries in a Search Engine Log,” Proc.First Int’l Workshop Location and the Web (LocWeb), 2008.
[10] T. Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, 2002.
[11] K.W.-T. Leung, D.L. Lee, and W.-C. Lee, “Personalized Web Search with Location Preferences,” Proc. IEEE Int’l Conf. Data Mining (ICDE), 2010.
[12] K.W.-T. Leung, W. Ng, and D.L. Lee, “Personalized Concept-Based Clustering of Search Engine Queries,” IEEE Trans. Knowledge and Data Eng., vol. 20, no. 11, pp. 1505-1518, Nov. 2008.
[13] H. Li, Z. Li, W.-C. Lee, and D.L. Lee, “A Probabilistic Topic-Based Ranking Framework for Location-Sensitive Domain Information Retrieval,” Proc. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2009.
[14] B. Liu, W.S. Lee, P.S. Yu, and X. Li, “Partially Supervised Classification of Text Documents,” Proc. Int’l Conf. Machine Learning (ICML), 2002.
[15] W. Ng, L. Deng, and D.L. Lee, “Mining User Preference Using Spy Voting for Search Engine Personalization,” ACM Trans. Internet Technology, vol. 7, no. 4, article 19, 2007.
[16] J.Y.-H. Pong, R.C.-W.Kwok, R.Y.-K.Lau, J.-X.Hao, and P.C.-C. Wong, “A Comparative Study of Two Automatic Document Classification Methods in a Library Setting,” J. Information Science, vol. 34, no. 2, pp. 213-230, 2008.
[17] C.E. Shannon, “Prediction and Entropy of Printed English,” Bell Systems Technical J., vol. 30, pp. 50-64, 1951.
[18] Q. Tan, X. Chai, W. Ng, and D. Lee, “Applying Co-Training to Clickthrough Data for Search Engine Adaptation,” Proc. Int’l Conf. Database Systems for Advanced Applications (DASFAA), 2004.

Click through data, concept, location search, mobile search engine, ontology, personalization, and user profiling.