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An Implementation of an Enhanced Web Graph Search Engine based on User Profiles and Clickthrough Patterns

Published on June 2015 by Rushikesh M. Shete, Dhiraj D. Shirbhate
National Conference on Recent Trends in Computer Science and Engineering
Foundation of Computer Science USA
MEDHA2015 - Number 2
June 2015
Authors: Rushikesh M. Shete, Dhiraj D. Shirbhate
c0052104-1ee3-43db-8b7e-eff2d7d76cc1

Rushikesh M. Shete, Dhiraj D. Shirbhate . An Implementation of an Enhanced Web Graph Search Engine based on User Profiles and Clickthrough Patterns. National Conference on Recent Trends in Computer Science and Engineering. MEDHA2015, 2 (June 2015), 7-13.

@article{
author = { Rushikesh M. Shete, Dhiraj D. Shirbhate },
title = { An Implementation of an Enhanced Web Graph Search Engine based on User Profiles and Clickthrough Patterns },
journal = { National Conference on Recent Trends in Computer Science and Engineering },
issue_date = { June 2015 },
volume = { MEDHA2015 },
number = { 2 },
month = { June },
year = { 2015 },
issn = 0975-8887,
pages = { 7-13 },
numpages = 7,
url = { /proceedings/medha2015/number2/21432-8024/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Trends in Computer Science and Engineering
%A Rushikesh M. Shete
%A Dhiraj D. Shirbhate
%T An Implementation of an Enhanced Web Graph Search Engine based on User Profiles and Clickthrough Patterns
%J National Conference on Recent Trends in Computer Science and Engineering
%@ 0975-8887
%V MEDHA2015
%N 2
%P 7-13
%D 2015
%I International Journal of Computer Applications
Abstract

As the exponential explosion of various contents generated on the Web Recommendation techniques have become increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies, music, images, books recommendations, query suggestions, tags recommendations, etc. In this paper, aim is to providing a general framework on user profiles & Clickthrough patterns. Firstly proposing a method which propagates similarities between different nodes i. e. from user profiles and generates recommendations from Clickthrough data. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including query suggestions, tag recommendations, expert finding, image recommendations etc. The experimental analysis on large data sets will show the promising future of our work.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Recommendations Query Suggestions Clickthrough Data User Profiles