Computational advertising: business models, technologies and issues (CoAd)
published: April 15, 2010, recorded: September 2009, views: 10157
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Internet advertising revenues in the United States totaled $21 billion for 2007, up 25 percent versus 2006 revenues of $16.9 billion (according to the Interactive Advertising Bureau); this represents approximately half the worldwide revenue from online advertising. Fueled by these growth rates and the desire to provide added incentives and opportunities for both advertisers and publishers, alternative business models to online advertising are been developed. This tutorial will review the main business models of online advertising including: the pay-per-impression model (CPM); and the pay-per-click model (CPC); a relative new comer, the pay-per-action model (CPA), where an action could be a product purchase, a site visit, a customer lead, or an email signup; and dynamic CPM (dCPM) which optimizes a campaign towards the sites and site sections that perform best for the advertiser. This tutorial will also discuss in detail the technology being leveraged to automatically target ads within these business models; this largely derives from the fields of machine learning (e.g., logistic regression, online learning), statistics (e.g., binomial maximum likelihood), information retrieval (vector space model, BM25), optimization theory (linear and quadratic programming), economics (auction mechanisms, game theory). Challenges such as click fraud (the spam of online advertising), deception, privacy and other open issues will also be discussed. Web 2.0 applications such as social networks, and video/photo-sharing pose new challenges for online advertising. These will also be discussed.
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