Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture
The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture
Prediction, Learning, and Games
Prediction, Learning, and Games
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Efficient bandit algorithms for online multiclass prediction
Proceedings of the 25th international conference on Machine learning
Explore/Exploit Schemes for Web Content Optimization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Learning to trade off between exploration and exploitation in multiclass bandit prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Data-driven multi-touch attribution models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Factoring past exposure in display advertising targeting
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Hybrid-ε-greedy for mobile context-aware recommender system
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A contextual-bandit algorithm for mobile context-aware recommender system
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Ad click prediction: a view from the trenches
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A game- heoretic machine learning approach for revenue maximization in sponsored search
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of online data inevitably makes an advertising system choose between maximizing its expected revenue according to its current knowledge in short term (exploitation) and trying to learn more about the unknown to improve its knowledge (exploration), since the latter might increase its revenue in the future. The exploitation and exploration (EE) tradeoff has been extensively studied in the reinforcement learning community, however, not been paid much attention in online advertising until recently. In this paper, we develop two novel EE strategies for online advertising. Specifically, our methods can adaptively balance the two aspects of EE by automatically learning the optimal tradeoff and incorporating confidence metrics of historical performance. Within a deliberately designed offline simulation framework we apply our algorithms to an industry leading performance based contextual advertising system and conduct extensive evaluations with real online event log data. The experimental results and detailed analysis reveal several important findings of EE behaviors in online advertising and demonstrate that our algorithms perform superiorly in terms of ad reach and click-through-rate (CTR).