Utilizing Verbal Intent in Semantic Contextual Advertising

Abstract

Existing semantic approaches to contextual advertising effectively match relevant ads to webpages in terms of topical intent. The authors observe, however, that there's still much room for improvement. In this article, they seek to utilize the verbal intent, which complements topical intent, in semantic contextual advertising. Verbal intent describes what a user wants to do, that is, the action perspective, with topical intent. The authors propose a methodology that effectively identifies verbal intent in webpages and ads, and then incorporates the verbal intent into an ad-ranking framework. The results of performance evaluation on real-world datasets clearly show the efficacy of (verb, topic) associations. Compared with state-of-the-art techniques, the proposed methodology exhibits a significant improvement of precision at a high level of recall in ad ranking, as well as a precision improvement of 35 percent on average in verb identification.