A personalized web page content filtering model based on segmentation
This addresses the need for fine-grained content filtering, especially for minors, by improving over traditional all-or-nothing blocking methods, though it is incremental in approach.
The paper tackles the problem of coarse web page blocking by proposing a personalized content filtering model that segments web pages to block only inappropriate segments rather than entire pages, achieving 88% accuracy in filtering.
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.