Learning to rank audience for behavioral targeting in display ads

ABSTRACT

Behavioral targeting (BT), which aims to sell advertisers those behaviorally related user segments to deliver their advertisements, is facing a bottleneck in serving the rapid growth of long tail advertisers. Due to the small business nature of the tail advertisers, they generally expect to accurately reach a small group of audience, which is hard to be satisfied by classical BT solutions with large size user segments. In this paper, we propose a novel probabilistic generative model named Rank Latent Dirichlet Allocation (RANKLDA) to rank audience according to their ads click probabilities for the long tail advertisers to deliver their ads. Based on the basic assumption that users who clicked the same group of ads will have a higher probability of sharing similar latent search topical interests, RANKLDA combines topic discovery from users' search behaviors and learning to rank users from their ads click behaviors together. In computation, the topic learning could be enhanced by the supervised information of the rank learning and simultaneously, the rank learning could be better optimized by considering the discovered topics as features. This co-optimization scheme enhances each other iteratively. Experiments over the real click-through log of display ads in a public ad network show that the proposed RANKLDA model can effectively rank the audience for the tail advertisers.

References

1. D. Blei and J. Lafferty. Latent dirichlet allocation. In Journal of Machine Learning Research, pages 993--1022, 2003.
2. D. Blei and J. Lafferty. Supervised topic models. In Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, 2007.
3. J.Chang and D. Blei. Relation topic models for document networks. Artificial Intelligence and Statistics, 2009.
4. T. Chen, J. Yan, G.Xue, and Z. Cheng. Transfer learning for behavioral targeting. In Proceedings of the 19th International Conference on World Wide Web, pages 1077--1078, 2010.
5. Y. Chen, D. Pavlov, and J. F.Canny. Large-Scale Behavioral Targeting. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 209--218, 2009.
6. X. Gu, S. Yang, and H. Li. Named entity mining from click-through data using weakly supervised latent dirichlet allocation. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 267--274, 2009.
7. M. Jordan, editor. Learning in Graphical Models. MIT Press, Cambridge, MA, 1999.
8. T. Jaakkola. Variational methods for inference and estimation in graphical methods. PhD thesis, MIT. 1997.
9. N. Liu, J. Yan, D. Shen, D. Chen, Z. Chen, and Y. Li. Learning to rank audience for behavioral targeting. In Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 719--720, 2010.
10. Y. Liu, A. Niculescu-Mizil, and W. Grys. Topic-Link LDA: joint models for topic and author community. In Proceedings of the 26th Annual International Conference on Machine Learning, 2009.
11. 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, pages 261--270, 2009.
12. Wikipedia. http://en.wikipedia.org/wiki/Display_advertising

Index Terms

• Information systems

  • Information retrieval

    • Retrieval models and ranking