A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Learning a meta-level prior for feature relevance from multiple related tasks
Proceedings of the 24th international conference on Machine learning
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
How much can behavioral targeting help online advertising?
Proceedings of the 18th international conference on World wide web
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective method for combating malicious scripts clickbots
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
Fast online learning through offline initialization for time-sensitive recommendation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-Scale Customized Models for Advertisers
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Marketing campaign evaluation in targeted display advertising
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
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In performance based display advertising, campaign effectiveness is often measured in terms of conversions that represent some desired user actions like purchases and product information requests on advertisers' website. Hence, identifying and targeting potential converters is of vital importance to boost campaign performance. This is often accomplished by marketers who define the user base of campaigns based on behavioral, demographic, search, social, purchase, and other characteristics. Such a process is manual and subjective, it often fails to utilize the full potential of targeting. In this paper we show that by using past converted users of campaigns and campaign meta-data (e.g., ad creatives, landing pages), we can combine disparate user information in a principled way to effectively and automatically target converters for new/existing campaigns. At the heart of our approach is a factor model that estimates the affinity of each user feature to a campaign using historical conversion data. In fact, our approach allows building a conversion model for a brand new campaign through campaign meta-data alone, and hence targets potential converters even before the campaign is run. Through extensive experiments, we show the superiority of our factor model approach relative to several other baselines. Moreover, we show that the performance of our approach at the beginning of a campaign's life is typically better than the other models even when they are trained using all conversion data after the campaign has completed. This clearly shows the importance and value of using historical campaign data in constructing an effective audience selection strategy for display advertising.