Improving Search Results Through Reducing Replica in User Profile

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Most important visible component of the internet contains millions of web pages waiting to present information on an amazing collection of topics. The search engine has played an increasingly important role. Nowadays, the search engine bases string matching has inherent harms like a low accuracy, inadequate individual support, duplicates document and hyperlinks in the user profile and so on. The present proposed work introduces a method against previously proposed personalized query clustering method in our research. The Experimental results show that a profile captures and utilize both user’s replica and non replicated preferences. The non replica of hyper, sub-hyperlinks and cluster content performs the best of among results. An important result from the experiments is that profiles with replica of links can increase the separation between similar and different queries. The separation provides a clear threshold for a LINGO clustering algorithm to terminate and improve the overall quality of the resulting query clusters and hyper, sub-hyper links
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Cluster Content; Search Result; User Profile; Replica; Hyperlinks; Sub-Hyperlinks

Full Text:



Tommy W. S. Chow and M. K. M. Rahman, Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection IEEE transactions on Neural Networks 2009, pp 1385–1402, 2009.

Q. Tan, X. Chai, W. Ng, and D. Lee, Applying Co-training to Click through Data for Search Engine Adaptation, Conference Proceedings on Database Systems for Advanced Applications (DASFAA): Springer-Verlag Berlin Heridelberg2004, pp 519-533, 2004.

Jie Yuan, Xinzhong Zhu, Jianmin Zhao, Huiying Xu , An Individual WEB Search Framework Based on User Profile and ClusteringAnalysis, First IEEE International Conference on Ubi-Media Computing 2008, pp 106-112, 2008.

Kenneth Wai-Ting Leung, Wilfred Ng, and Dik Lun Lee, Personalized Concept-Based Clustering of Search Engine Queries, IEEE Transactions on Knowledge and Data Engineering, Vol 20, N. 11, pp 1505-1518, 2008.

Kenneth Wai-Ting Leung and Dik Lun Lee, Deriving Concept-Based User Profiles from Search Engine Logs, IEEE Transactions on Knowledge and Data Engineering, Vol. 22, N. 7, pp 969-982, 2010.

Mingli FENG, Yajun DU Mingjun FENG Yingyu WANG, Personalized user-query semantic clustering using search click information, IEEE International Conference on Management and Service Science MASS '09, pp 1-4, 2009.

Geraci, Filippo, Marco Pellegrini, Marco Maggini, and Fabrizio Sebastiani, Cluster Generation and Cluster Labeling for Web Snippets, 13th International Conference, SPIRE 2006: Springer Berlin Heidelberg, 2006, pp 25-36.

Wei, ZHANG, XU Baowen, ZHANG Weifeng, and XU Junling. ISTC: A New Method for Clustering Search Results, Journal of Natural Sciences, Vol 04, pp 501-04, 2008.

Oikonomakou, Nora, and Michalis Vazirgiannis, A Review of Web Document Clustering Approache, Data Mining and Knowledge Discovery Handbook, 2010.

Ahmed Sameh, Amar Kadray, Semantic Web Search Results Clustering Using Lingo and Word Net, International Journal of Research and Reviews in Computer Science (IJRRCS), Vol. 1, N. 2, pp 71-76, 2010.

Osinski, Stanislaw, and Dawid Weiss, Conceptual Clustering Using Lingo Algorithm: Evaluation on Open Directory Project Data,. Proceedings of the International IIS: IIPWM´04 Conference, pp 369-378, 2004.

B. Liu, Web Data Mining-Exploring Hyperlinks, Contents, and Usage Data, Springer Series on Data-Centric Systems and Applications, 2007.

Wei Jiang, Mummoorthy Murugesan, Chris Clifton, Luo Si Similar Document Detection with Limited Information Disclosure, Proceedings of the 24th International Conference on Data Engineering: IEEE ,pp 7-12, 2008.

S. Kotsiantis, E. Athanasopoulou, P. Pintelas, Logitboost of Multinomial Bayesian Classifier for Text Classification, (2006) International Review on Computers and Software (IRECOS), 1 (3), pp. 243 – 250.

Abdi, A., Asghari, S.A., Pourmozaffari, S., Taheri, H., Pedram, H., An optimum instruction level method for soft error detection, (2012) International Review on Computers and Software (IRECOS), 7 (2), pp. 637-641.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize