ABSTRACT
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.
- N. Archak, V. S. Mirrokni, and S. Muthukrishnan. Mining advertiser-specific user behavior using adfactors. In Proceedings of the 19th International World Wide Web Conference, 2010. Google ScholarDigital Library
- A. Bagherjeiran, A. O. Hatch, and A. Ratnaparkhi. Ranking for the conversion funnel. In Proceeding of the 33rd SIGIR conference on Research and development in information retrieval, 2010. Google ScholarDigital Library
- A. Bagherjeiran, A. O. Hatch, A. Ratnaparkhi, and R. Parekh. Large-scale customized models for advertisers. In ICDM Workshops, 2010. Google ScholarDigital Library
- Y. Chen, D. Pavlov, and J. Canny. Large-scale behavioral targeting. In Proceedings of the 15th SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009. Google ScholarDigital Library
- I. Click Forensics. Click fraud index. http://www.clickforensics.com/resources/click-fraud-index.html, 2010.Google Scholar
- Google, Inc. Google analytics. http://www.google.com/analytics.Google Scholar
- A. Hatch, A. Bagherjeiran, and A. Ratnaparkhi. Clickable terms for contextual advertising. In ADKDD, 2010.Google Scholar
- I. Nielsen Company. Nielsen Claritas PRIZM. http://en-us.nielsen.com/tab/product_families/nielsen_claritas/prizm.Google Scholar
- Y. Peng, L. Zhang, M. Chang, and Y. Guan. An effective method for combating malicious scripts clickbots. In Proceedings of the 14th European Symposium on Research in Computer Security, 2009. Google ScholarDigital Library
- B. J. Pine. Mass customizing products and services. Strategy & Leadership, 21(4):6 -- 55, 1993.Google Scholar
- F. Provost, B. Dalessandro, R. Hook, X. Zhang, and A. Murray. Audience selection for on-line brand advertising: privacy-friendly social network targeting. In Proceedings of the 15th SIGKDD international conference on Knowledge discovery and data mining, 2009. Google ScholarDigital Library
- B. Rey and A. Kannan. Conversion rate based bid adjustment for sponsored search auctions. In Proceedings of the 19th International World Wide Web Conference, 2010. Google ScholarDigital Library
- X.-R. Wang, K.-W. Chang, C.-J. Hsieh, R.-E. Fan, G.-X. Yuan, H.-F. Yu, F.-L. Huang, and C.-J. Lin. Liblinear -- a library for large linear classification. http://www.csie.ntu.edu.tw/cjlin/liblinear/.Google Scholar
- J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. How much can behavioral targeting help online advertising? In Proceedings of the 18th International Conference on World Wide Web, 2009. Google ScholarDigital Library
- L. Zhang and Y. Guan. Detecting click fraud in pay-per-click streams of online advertising networks. In Proceedings of the 28th IEEE International Conference on Distributed Computing Systems, 2008. Google ScholarDigital Library
Index Terms
- Learning to target: what works for behavioral targeting
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