@techreport{TD:100916,
	att_abstract={{Ever wonder why that Kia Ad ran during Iron Chef? 
While advertising on television is still a robust business, providing a fascinating mix of marketing, branding, predictive modeling and measurements, it is at risk with the recent emergence of online television. Traditional methods used to generate advertising 
campaigns on television do not come close to the highly sophisticated computational techniques being used in the online world, in terms of efficiency. This paper is an attempt to recast the process of television advertising media campaign generation in a computational framework. We describe efficient mathematical approaches to solve for the task of finding optimal campaigns for specific target audiences. We highlight the efficacy of our proposed methods and compare them using two case studies against 
campaigns generated by traditional methods. }},
	att_authors={sb799t, sc984q, da1287, su2464},
	att_categories={C_CCF.5, C_CCF.8, C_IIS.2},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012. {{, 2012-12-12}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Machine Learning,  Data-Mining,  Computational Advertising,  Television Campaigns,  U-verse},
	att_techdoc={true},
	att_techdoc_key={TD:100916},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100916_DS1_2012-10-15T18:30:23.600Z.pdf},
	author={Suhrid Balakrishnan and Sumit Chopra and David Applegate and Simon Urbanek},
	institution={{IEEE International Conference on Data Mining}},
	month={December},
	title={{Computational Television Advertising}},
	year=2012,
}