ADAPTIVE WEB BROWSER
Main Article Content
Keywords
Machine learning, WW.
Abstract
Web browser play important role in World Wide Web (WWW). We go through different website and invest enough time searching relevant URL. The project deals with making a browser that will assist a person to find relevant information satisfying long term recurring goals rather than short term goals and describe our research on learning browser behaviour model for predicting the current information need of web user. Depending upon the user sequence of browsing behaviour it indicates the degree to which page content satisfies user’s need. Thus one’s search experience may be used to help the next users to reduce their searching effort. So, through more and more searching greater experience will be gained by browser. We deploy extensive use of machine learning for the browser to learn user’s behaviour. By such model the searching ability of browser becomes more efficient and faster thus resulting in an intelligent and adaptive web browser.
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References
1. Adaptive Web Browser: An Intelligent Browser Md. Forhad Rabbi, Tanveer Ahmed, Anindya
Roy Chowdhury, Md. Ran-O-Beer Islam Department of Computer Science & Engineering Shah Jalal
University of Science & Technology Sylhet, Bangladesh
2. Identifying Machine Query for an Intelligent Web Browser System Tingshao Zhu, Graduate
University ofChinese Academy of SciencesBeijing, China XinguoXu, National Computer System
EngineeringResearch Institute of ChinaBeijing, China Guohua Liu, Department of Computing
ScienceUniversity of AlbertaEdmonton, Canada
3. Machine learning for better Web browsing DunjaMladeni, Dept. of Intelligent Systems,
J.StefanInstituteJamova 39, 1000 Ljubljana, Slovenia
4. A Platform for Large-Scale Machine Learning on Web Design ArvindSatyanarayan, SAP Stanford
Graduate Fellow Dept. of Computer Science Stanford University 353 Serra Mall Stanford, CA 94305
USA Maxine Lim, Dept. of Computer Science Stanford University 353 Serra Mall Stanford, CA
94305 USA Scott R. Klemmer, Dept. of Computer Science Stanford University353 Serra Mall
Stanford, CA 94305 USA
Roy Chowdhury, Md. Ran-O-Beer Islam Department of Computer Science & Engineering Shah Jalal
University of Science & Technology Sylhet, Bangladesh
2. Identifying Machine Query for an Intelligent Web Browser System Tingshao Zhu, Graduate
University ofChinese Academy of SciencesBeijing, China XinguoXu, National Computer System
EngineeringResearch Institute of ChinaBeijing, China Guohua Liu, Department of Computing
ScienceUniversity of AlbertaEdmonton, Canada
3. Machine learning for better Web browsing DunjaMladeni, Dept. of Intelligent Systems,
J.StefanInstituteJamova 39, 1000 Ljubljana, Slovenia
4. A Platform for Large-Scale Machine Learning on Web Design ArvindSatyanarayan, SAP Stanford
Graduate Fellow Dept. of Computer Science Stanford University 353 Serra Mall Stanford, CA 94305
USA Maxine Lim, Dept. of Computer Science Stanford University 353 Serra Mall Stanford, CA
94305 USA Scott R. Klemmer, Dept. of Computer Science Stanford University353 Serra Mall
Stanford, CA 94305 USA
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Published
Sep 26, 2015
Article Details
How to Cite
Parashar, A., Mali, M., Kumar, R., & Ambadekar, S. (2015). ADAPTIVE WEB BROWSER. International Journal of Students’ Research in Technology & Management, 1(2), 203-206. Retrieved from http://giapjournals.com/index.php/ijsrtm/article/view/61
Issue
Section
IT & Computer Engineering
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