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
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
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
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
Large-Scale Customized Models for Advertisers
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Web-scale user modeling for targeting
Proceedings of the 21st international conference companion on World Wide Web
Enabling direct interest-aware audience selection
Proceedings of the 21st ACM international conference on Information and knowledge management
Web-scale multi-task feature selection for behavioral targeting
Proceedings of the 21st ACM international conference on Information and knowledge management
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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Understanding what interests and delights users is critical to effective behavioral targeting, especially in information-poor contexts. As users interact with content and advertising, their passive behavior can reveal their interests towards advertising. Two issues are critical for building effective targeting methods: what metric to optimize for and how to optimize. More specifically, we first attempt to understand what the learning objective should be for behavioral targeting so as to maximize advertiser's performance. While most popular advertising methods optimize for user clicks, as we will show, maximizing clicks does not necessarily imply maximizing purchase activities or transactions, called conversions, which directly translate to advertiser's revenue. In this work we focus on conversions which makes a more relevant metric but also the more challenging one. Second is the issue of how to represent and combine the plethora of user activities such as search queries, page views, ad clicks to perform the targeting. We investigate several sources of user activities as well as methods for inferring conversion likelihood given the activities. We also explore the role played by the temporal aspect of user activities for targeting, e.g., how recent activities compare to the old ones. Based on a rigorous offline empirical evaluation over 200 individual advertising campaigns, we arrive at what we believe are best practices for behavioral targeting. We deploy our approach over live user traffic to demonstrate its superiority over existing state-of-the-art targeting methods.