What is computational advertising?
- A principled way to find the "best match" between a user in a context and a suitable ad. Computational advertising is an emerging new scientific sub-discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, classification, optimization, and microeconomics.
What is the central challenge of computational advertising?
- To find the "best match" between a given user in a given context and a suitable advertisement. Depending on the definition of "best match" this challenge leads to a variety of massive optimization and search problems, with complicated constraints.
Which types of Advertising are there?
- Brand advertising: the goal is to create a distinct favorable image of the brand
- Direct marketing: the goal is to create a certain "direct response" - buy, subscribe, vote, donate etc.
What is the difference between classical and computational advertising?
Compared to classical advertising, computational advertising is more measurable, more efficient and more targetable:
- have more venues: all different kind of Internet websites/Apps
- more different ad format
- Possibility of personalization
- Tiny cost per opportunity
- Quantifiable
Who are the key participants?
- Advertiser (Demand): For Advertiser is ROI important. Ads should bring immediate value (profit for current event) or/and future value (increase of the future profit due to the user action)
- Users: Ad relevancy
- Publishers (Supply): Revenue increase through advertising
- "Middle Man"- Match maker
Reference:
Broder, Andrei (2008): Computational advertising. Online available: https://dl.acm.org/citation.cfm?id=1347190
Broder, Andrei & Josifovski, Vanja (2011): Introduction to Computational Advertising. Online available: https://web.stanford.edu/class/msande239/
Dave, Kushal & Varma, Vasudeva (2014): Computational Advertising: Techniques for Targeting Relevant Ads. Foundations and Trends® in Information Retrieval: Vol. 8: No. 4–5, pp 263-418. http://dx.doi.org/10.1561/1500000045
