Yahoo Fellow, VP, and computational advertising guru Andrei Broder gave a talk at CIKM 2008 on "The Evolving Computational Advertising Landscape" with some notable details that were missing from his previous talks on this topic.
Specifically, Andrei described "the recommender system connection" with advertising where we want "continuous CTR feedback" for each (query, ad) pair to allow us to learn the "best match between a given user in a given context and a suitable advertisement". He said this was "closest to a recommender system" because, to overcome sparse data and get the best match, we need to find similar ads, pages, and users.
At this point, Andrei offered a bit of a tease that an upcoming (and not yet available) paper that has Yehuda Koren as a co-author will talk more on this topic of treating advertising as a recommender problem. Yehuda Koren recently joined Yahoo Research and is one of the members of the top-ranked team in the Netflix recommender contest.
Andrei continued on the theme of personalization and recommendations for advertising, talking briefly about personalized ad targeting and saying that he thought short-term history (i.e. the last few queries) likely would be more useful than long-term history (i.e. a subject-interest profile).
Andrei also talked about several other topics that, while covered in his older talks, also are quite interesting. He contrasted computational advertising and classical advertising, saying that the former uses billions of ads and venues, has liquid market, and is personalizable, measurable, and low cost. He described the four actors in an advertising market -- advertisers, ad agencies, publishers, and users -- and said they advertising engines have the difficult task of optimizing the four separate and possibly conflicting utility functions of these actors. He talked about the ideal of "advertising as a form of information" rather than as an annoyance, the key there being making it as useful and relevant as possible. And, he spent some time on mobile advertising, talking about the very interesting but slightly scary possibilities of using precise locations of individuals and groups over time to do "instant couponing" to nearby stores (where what we mean by nearby is determined by your current speed and whether that makes it clear that you are in a car), to recognize which stores are open and which stores are popular, to predict lifestyle choices and interests of individuals and groups, and to make recommendations.
This talk was one of the ones recorded by videolectures.net and should appear there in a week or so.
Please see also my previous posts on related topics including "Google describes perfect advertising", "Recommending advertisements", and "What to advertise when there is no commercial intent?"