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Reseach Article

Intelligent Search Engine Ranking Algorithm inspired by Recommendation Engines

by Ganesh Venkataraman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 8
Year of Publication: 2014
Authors: Ganesh Venkataraman
10.5120/14860-3231

Ganesh Venkataraman . Intelligent Search Engine Ranking Algorithm inspired by Recommendation Engines. International Journal of Computer Applications. 85, 8 ( January 2014), 16-18. DOI=10.5120/14860-3231

@article{ 10.5120/14860-3231,
author = { Ganesh Venkataraman },
title = { Intelligent Search Engine Ranking Algorithm inspired by Recommendation Engines },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 8 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 16-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number8/14860-3231/ },
doi = { 10.5120/14860-3231 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:01:55.647379+05:30
%A Ganesh Venkataraman
%T Intelligent Search Engine Ranking Algorithm inspired by Recommendation Engines
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 8
%P 16-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Every step in the evolution of human kind is associated with the inherent quest for knowledge and substantial growth in intelligence. In the modern world, the thirst for information is quenched by search engines that crawl billions of pages on the World Wide Web. This paper endeavors to make the ranking of the indexed web pages more intelligent by using techniques followed by recommendation engines that, with the help of some algorithms, recommend products on e-commerce websites. The focus primarily lies on discovering user groups, finding the degree of similarity between users based on search queries and building a graph that tracks the clicks on search results within the group, enabling the machine to learn which result might meet the expectation of one particular user and rank the results accordingly.

References
  1. Algorithms of the Intelligent Web, Haralambos Marmanis and Dmitry Babenko,
  2. Programming Collective Intelligence- Building Smart Web 2. 0 Applications, Toby Segaran, 2007
  3. A Survey of Ranking Algorithms, Alessio Signorini, Department of Computer Science, University of Iowa, September 11, 2005
  4. S. Brin, L. Page, The anatomy of a large-scale hyper textual web search engine, Proceedings of the 7th International World Wide web Conference, 1998
  5. Pearson's Correlation Coefficient: http://faculty. uncfsu. edu/dwallace/lesson%2017. pdf
  6. The Backpropogation algorithm. http://page. mi. fu-berlin. de/rojas/neural/chapter/K7. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Search engine ranking algorithms intelligent ranking recommendation systems.