New metrics for new media: toward the development of Web measurement standards
World Wide Web Journal - Special issue on advancing HTML: style and substance
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Multiple Subset Sum Problem
SIAM Journal on Optimization
IEEE Intelligent Systems
The Role of the Management Sciences in Research on Personalization
Management Science
Performance bounds of algorithms for scheduling advertisements on a web page
Journal of Scheduling
Scheduling Banner Advertisements on the Web
INFORMS Journal on Computing
Optimal Internet Media Selection
Marketing Science
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Expert Systems with Applications: An International Journal
Effects of Information Revelation Policies Under Cost Uncertainty
Information Systems Research
A framework for intermediated online targeted advertising with banner ranking mechanism
Information Systems and e-Business Management
Optimizing direct response in Internet display advertising
Electronic Commerce Research and Applications
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The increasing popularity of the world wide web has made it an attractive medium for advertisers. As more advertisers place internet advertisements (hereafter also called "ads), it has become important for web site owners to maximize revenue through the optimal selection and placement of these ads. Unlike most previous research, we consider a hybrid pricing model where the price advertisers pay is a function of (i) the number of exposures of the ad and (ii) the number of clicks on the ad. The problem is to find an ad schedule to maximize web site revenue under a hybrid pricing model. We formulate two versions of the problem: static and dynamic, and propose a variety of efficient solution techniques that provide near-optimal solutions. In the dynamic version, the schedule of ads is changed based on individual user click behavior. We show - using a theoretical proof under special circumstances and an experimental demonstration under general conditions - that a schedule that adapts to user click behavior consistently outperforms one that does not. We also demonstrate that to benefit from observing user click behavior, the associated probability parameter need not be estimated accurately.