Proceedings of the 20th international conference companion on World wide web
Learning to target: what works for behavioral targeting
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding the right consumer: optimizing for conversion in display advertising campaigns
Proceedings of the fifth ACM international conference on Web search and data mining
Targeting converters for new campaigns through factor models
Proceedings of the 21st international conference on World Wide Web
Web-scale user modeling for targeting
Proceedings of the 21st international conference companion on World Wide Web
Factoring past exposure in display advertising targeting
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a robust modeling of temporal interest change patterns for behavioral targeting
Proceedings of the 22nd international conference on World Wide Web
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Performance advertisers want to maximize the return on their advertising spend. In the online advertising world, this means showing the ad only to those users most likely to convert i.e. buy a product or service. Existing ad targeting solutions such as context targeting and rule-based segment targeting primarily leverage marketing intuition to identify audience segments that would be likely to convert. Even the more sophisticated model-based approaches such as behavioral targeting identify audience segments interested in certain coarse-grained categories defined by the publisher. Advertisers are now able, through beaconing, to tell us exactly who their preferred customers are. Advertisers want to augment their existing advertising campaign with custom models that learn from the campaign and focus on attracting new users. Motivated by our experience with advertisers, we pose this problem within the context of ensemble learning. Building custom models for an existing ad campaign can be viewed as operations on an ensemble classifier: add, modify, or complement a classifier. An ideal new classifier should incrementally improve the ensemble and minimize overlap with any existing classifiers already in the ensemble–it should learn something new. With the proposed approach we are able to augment the advertising campaigns of several large advertisers at a large online advertising company.