Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
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
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
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Collaborative filtering with temporal dynamics
Communications of the ACM
User profiles for personalized information access
The adaptive web
Dynamic adaptation strategies for long-term and short-term user profile to personalize search
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Mining advertiser-specific user behavior using adfactors
Proceedings of the 19th international conference on World wide web
A characterization of online browsing behavior
Proceedings of the 19th international conference on World wide web
A contextual-bandit approach to personalized news article recommendation
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
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Retrieval models for audience selection in display advertising
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning to target: what works for behavioral targeting
Proceedings of the 20th ACM international conference on Information and knowledge management
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
Web-scale user modeling for targeting
Proceedings of the 21st international conference companion on World Wide Web
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Modern web-scale behavioral targeting platforms leverage historical activity of billions of users to predict user interests and inclinations, and consequently future activities. Future activities of particular interest involve purchases or transactions, and are referred to as conversions. Unlike ad-clicks, conversions directly translate to advertiser's revenue, and thus provide a very concrete metric for return on advertising investment. A typical behavioral targeting system faces two main challenges: the web-scale amounts of user histories to process on a daily basis, and the relative sparsity of conversions (compared to clicks in a traditional setting). These challenges call for generation of effective and efficient user profiles. Most existing works use the historical intensity of a user's interest in various topics to model future interest. In this paper we explore how the change in user behavior can be used to predict future actions and show how it complements the traditional models of decaying interest and action recency to build a complete picture about the user interests and better predict conversions. Our evaluation over a real-world set of campaigns indicates that the combination of change of interest, decaying intensity, and action recency helps in: 1) scoring significant improvements in optimizing for conversions over traditional baselines, 2) substantially improving the targeting efficiency for campaigns with highly sparse conversions, and 3) highly reducing the overall history sizes used in targeting. Furthermore, our techniques have been deployed to production and scored a substantial improvement in targeting performance while imposing a negligible overhead in terms of overall platform running time.