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AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive web search based on user profile constructed without any effort from users
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A large-scale analysis of query logs for assessing personalization opportunities
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Detecting Click Fraud in Pay-Per-Click Streams of Online Advertising Networks
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
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
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Mining advertiser-specific user behavior using adfactors
Proceedings of the 19th international conference on World wide web
Conversion rate based bid adjustment for sponsored search
Proceedings of the 19th international conference on World wide web
Extracting user profiles from large scale data
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
An effective method for combating malicious scripts clickbots
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
Ranking for the conversion funnel
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Predicting product adoption in large-scale social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Large-Scale Customized Models for Advertisers
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
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The ultimate goal of advertisers are conversions representing desired user actions on the advertisers' websites in the form of purchases and product information request. In this paper we address the problem of finding the right audience for display campaigns by finding the users that are most likely to convert. This challenging problem is at the heart of display campaign optimization and has to deal with several issues such as very small percentage of converters in the general population, high-dimensional representation of the user profiles, large churning rate of users and advertisers. To overcome these difficulties, in our approach we use two sources of information: a seed set of users that have converted for a campaign in the past; and a description of the campaign based on the advertiser's website. We explore the importance of the information provided by each of these two sources in a principled manner and then combine them to propose models for predicting converters. In particular, we show how seed set can be used to capture the campaign-specific targeting constraints, while the campaign metadata allows to share targeting knowledge across campaigns. We give methods for learning these models and perform experiments on real-world advertising campaigns. Our findings show that the seed set and the campaign metadata are complimentary to each other and both sources provide valuable information for conversion optimization.