ACAKS: An Ad-Collection-Aware Keyword Selection Approach for Contextual Advertising

Abstract

In this work we address the problem of selecting keywords from a web page in order to submit them to an ad selection system. Several previous research found in literature have proposed machine learning strategies to determine these keywords in different contexts, such as emails and web pages. Such machine learning approaches usually have the goal of selecting keywords considered as good by humans. We here propose a new machine learning strategy where the selection is driven by the expected impact of the keyword in the final quality of the ad placement system, which we name here as ad-collection-aware keyword selection. This new approach relies on the judgment of the users about the ads each keyword can retrieve. Although this strategy requires a higher effort to build the training set than previous approaches, we believe the gain obtained in recall is worth enough to make the ad collection aware approach a better choice. In experiments we performed with an ad collection and considering features proposed in a previous work, we found that the new ad-collection-aware approach led to a gain of 62% in the recall over the baseline without dropping the precision values. Besides the new alternative to select keywords, we also study the use of features extracted from the ad collection in the task of selecting keywords.