Using urls and table layout for web classification tasks
We propose new features and algorithms for automating Web-page classification tasks such as content recommendation and ad blocking. We show that the automated classification of Web pages can be much improved if, instead of looking at their textual content, we consider each links's URL and the visual placement of those links on a referring page. These features are unusual: rather than being scalar measurements like word counts they are tree structured---describing the position of the item in a tree. We develop a model and algorithm for machine learning using such tree-structured features. We apply our methods in automated tools for recognizing and blocking Web advertisements and for recommending "interesting" news stories to a reader. Experiments show that our algorithms are both faster and more accurate than those based on the text content of Web documents.