An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Targeted advertising on the Web with inventory management
Interfaces - Wagner prize papers
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Multi-armed bandit problems with dependent arms
Proceedings of the 24th international conference on Machine learning
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Just-in-time contextual advertising
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Pricing display ads and contextual ads: Competition, acquisition, and investment
Electronic Commerce Research and Applications
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Pricing of Online Advertising: Cost-Per-Click-Through Vs. Cost-Per-Action
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Pricing guaranteed contracts in online display advertising
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
OR PRACTICE---Scheduling of Dynamic In-Game Advertising
Operations Research
Retrieval models for audience selection in display advertising
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning to rank audience for behavioral targeting in display ads
Proceedings of the 20th ACM international conference on Information and knowledge management
Leveraging Wikipedia concept and category information to enhance contextual advertising
Proceedings of the 20th ACM international conference on Information and knowledge management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Online advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions but also lower the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and ads is a fine start but cannot solve the problem completely because good matching does not necessarily lead to good payoff. Moreover, with the limited display impressions, we need to balance the need of selecting ads to learn true ad payoffs (exploration) with that of allocating ads to generate high immediate payoffs based on the current belief (exploitation). In this paper, we address the problem by employing Partially observable Markov decision processes (POMDPs) and discuss how to utilize the correlation of ads to improve the efficiency of the exploration and increase ad incomes in a long run. Our mathematical derivation shows that the belief states of correlated ads can be naturally updated using a formula similar to collaborative filtering. To test our model, a real world ad dataset from a major search engine is collected and categorized. Experimenting over the data, we provide an analyse of the effect of the underlying parameters, and demonstrate that our algorithms significantly outperform other strong baselines.