A personalized web page content filtering model based on segmentation

TL;DR

This paper introduces a fine-grained web page content filtering model that segments pages to selectively block inappropriate content, improving over traditional all-or-nothing blocking methods with 88% accuracy.

Contribution

It presents a novel segmentation-based filtering approach that automatically identifies and blocks specific content segments on web pages, enhancing filtering precision.

Findings

88% accuracy in segment filtering

Fine-grained content blocking capability

Automatic identification of content segments

Abstract

In the view of massive content explosion in World Wide Web through diverse sources, it has become mandatory to have content filtering tools. The filtering of contents of the web pages holds greater significance in cases of access by minor-age people. The traditional web page blocking systems goes by the Boolean methodology of either displaying the full page or blocking it completely. With the increased dynamism in the web pages, it has become a common phenomenon that different portions of the web page holds different types of content at different time instances. This paper proposes a model to block the contents at a fine-grained level i.e. instead of completely blocking the page it would be efficient to block only those segments which holds the contents to be blocked. The advantages of this method over the traditional methods are fine-graining level of blocking and automatic identification of portions of the page to be blocked. The experiments conducted on the proposed model indicate 88% of accuracy in filtering out the segments.