2002•Unpublished venueRequires access

Prediction for Visiting Path on Web

Han Jing

Open publisher page 4 citations

Abstract

When Internet users are browsing the Web, they always have to wait for the next page to come up after selecting the link to the page, even though the Web server is sometimes idle. To reduce the perceived response time, the server is required to predict users?next HTTP request and pre-process the Web page with remaining CPU resources. This paper introduces various methods to predict users?next HTTP request using classified Web page information, user profiles, and Web logs. Sixteen algorithms and methods are analyzed and compared in this paper. Experimental results show that some methods produce highly probable results with the user of user profiles and classified Web page information.

About this research paper

What this paper is about

When Internet users are browsing the Web, they always have to wait for the next page to come up after selecting the link to the page, even though the Web server is sometimes idle. To reduce the perceived response time, the server is required to predict users?next HTTP request and pre-process the Web page with remaining CPU resources. This paper introduces various methods to predict users?next HTTP request using classified Web page information, user profiles, and Web logs. Sixteen algorithms and methods are analyzed and compared in this paper. Experimental results show that some methods produce highly probable results with the user of user profiles and classified Web page information.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

When Internet users are browsing the Web, they always have to wait for the next page to come up after selecting the link to the page, even though the Web server is sometimes idle. To reduce the perceived response time, the server is required to predict users?next HTTP request and pre-process the Web page with remaining CPU resources. This paper introduces various methods to predict users?next HTTP request using classified Web page information, user profiles, and Web logs. Sixteen algorithms and methods are analyzed and compared in this paper. Experimental results show that some methods produce highly probable results with the user of user profiles and classified Web page information.

Key concepts: Computer science, Web page, Static web page, World Wide Web, Web server, Page view, Web navigation, Web log analysis software

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