Novel Web Page Classification Techniques in Contextual Advertising

摘要

Contextual advertising seeks to place relevant ads to generic web pages based on their contents. Recently, it has been observed that classifying web pages into a well-organized taxonomy of topics is promising for matching topically relevant ads to web pages. Following the observation, in this paper we propose two methods to increase classification accuracy for web pages in the context of contextual advertising. Our strategy is to enhance the baseline classifier by reflecting unique features of web pages and the taxonomy. In particular, category tags extracted from web pages are utilized to augment term weights, and the hierarchical structure of the taxonomy is taken into account to categorize web pages with high confidence. We conduct a series of experiments to evaluate the proposed methods, and the results show that classification accuracy is increased up to 11% compared to the baseline classifier.