A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns

Abstract

Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been further complemented by recent work in Look-alike Modeling (LAM) which helps in creating models which are customized according to each advertiser's requirements and each campaign's characteristics, and which show ads to users who are most likely to convert on them, not just click them. In Look-alike Modeling given data about converters and nonconverters, obtained from advertisers, we would like to train models automatically for each ad campaign. Such custom models would help target more users who are similar to the set of converters the advertiser provides. The advertisers get more freedom to define their preferred sets of users which should be used as a basis to build custom targeting models.