Hybrid genetic algorithm and augmented neural network application for solving the online advertisement scheduling problem with contextual targeting

Authors:
Jason Deane
Affiliations:
Department of Business Information Technology, 1007 Pamplin Hall, College of Business, Virginia Tech, Blacksburg, VA 24061, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 13
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An entropy approach to unintrusive targeted advertising on the Web

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Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
A Framework for the Optimizing of WWW Advertising

TREC '98 Proceedings of the International IFIP/GI Working Conference on Trends in Distributed Systems for Electronic Commerce
Targeted advertising on the Web with inventory management

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Performance bounds of algorithms for scheduling advertisements on a web page

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Approximating the Advertisement Placement Problem

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Scheduling Banner Advertisements on the Web

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Improvements to the Linear Programming Based Scheduling of Web Advertisements

Electronic Commerce Research
Scheduling Web Advertisements: A Note on the Minspace Problem

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An Improved Augmented Neural-Network Approach for Scheduling Problems

INFORMS Journal on Computing
Optimal Scheduling and Placement of Internet Banner Advertisements

IEEE Transactions on Knowledge and Data Engineering
Scheduling web banner advertisements with multiple display frequencies

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

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Abstract

Worldwide growth of the online community continues to push the popularity of internet marketing. Fueled by this trend, the online advertising industry is experiencing unprecedented revenue growth. One of the most important drivers of this revenue is banner advertising, which has long been a staple of the online advertising industry. Previous research has introduced quantitative models and solution approaches for the challenging basic scheduling optimization problem. We extend this work by incorporating the most common and popular trend in the in the industry, online advertisement targeting. In addition, motivated by the NP-hard nature of the resulting problem, we propose and test several heuristic and metaheuristic based solution techniques for the proposed problem.