Learning in graphical models
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
The Journal of Machine Learning Research
How much can behavioral targeting help online advertising?
Proceedings of the 18th international conference on World wide web
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Named entity mining from click-through data using weakly supervised latent dirichlet allocation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning for behavioral targeting
Proceedings of the 19th international conference on World wide web
Learning to rank audience for behavioral targeting
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Sequential selection of correlated ads by POMDPs
Proceedings of the 21st ACM international conference on Information and knowledge management
CTR prediction for contextual advertising: learning-to-rank approach
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Hi-index | 0.00 |
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.