document: 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 .
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