Following are a few running notes from the KDD invited talk "Internet Advertising and Optimal Auction Design" by Dr. Michael Schwarz from Yahoo! Research:
Dr. Schwarz explains that the original advertising models were quite simple but now new tweaks and optimizations are being added to, for example, estimate click probabilities, match ads for long tail of queries, etc. So there is a "Fudge" factor in online advertising algorithms, and the question now is how to combine these correctly?
In sponsored search options space, Overture was really the pioneer of pay-per-click. In such a scheme, the bidder would pay whatever price that was fixed during the auction of an advertising position. However, soon people realized that generalized first auction is unstable. This is because if advertisers can keep changing their bids and the minimum bid keeps fluctuating. The situation is illustrated in the following paper and as shown in the image below:
This system was also prone to manipulation by automated robots quite easily.
Next, in 2002 Google came up with the Generalized Second-Price option (GSP): here the advertiser pays for the price fixed by the next highest bidder (plus a minor amount like a cent), upon winning an auction. Later Yahoo! followed. In this mechanism the assumption is that for the ad position i, clicks are almost independent of the advertisement. A clock shows the current price that keeps increasing, a bid price is fixed only at the time of dropping out and the final payments are computed according to GSP. Dr. Schwarz presents how this is related to "Generalized English Auctions". The intuition is that the advertisers values the price just at the point where the advertiser drops out of the game. If we think about it, at each point the advertiser is making a decision to go closer to the point where he values the position enough but not to a point where he/she would have regrets if the next highest bidder would drop out of the auction, causing a win. The GSP approach has been shown to have a unique equilibrium. For more details you can read the paper: "Internet advertising, generalized second price options".
Finally, the talk discusses an optimal matching problem. If you know the probability distribution from which the values for the bidders are drawn, in theory, the highest probable match (ie optimal auction) is deterministic. According to further studies in these advertising models, Dr. Schwarz suggests that despite multiple units the value for a slot, the auction is not dependent on the number of slots! This is great from a search engine's perspective. Although I guess this hold to a certain number of slots -- or else search results would have looked rather different! Further, the decline in CTR from one slot to another also does not have any influence on the auction. This is perhaps where clever data mining techniques can be applied to help estimate the probability distribution of bidder's values for each slot and ultimately, lead to setting optimal reserve price. I guess this is perhaps the point where search engines start guarding their trade secrets closely.
Finally, during discussions with many people at the conference, on theme that kept coming up is that of social advertising and monetization of social networks -- which has been a typically difficult problem. Personally, the way I see this (more of my 2 cents here) is three fold:
- One, are you advertising for the purpose of targeting an individual (based on the profile information)?
- Two, are you targeting an individual based on the actions of her friends and neighbors in the social graph?
- Three, are you targeting a group or a community in the network?
KDD is an exciting venue precisely because it is at the confluence of data mining, social media, large scale text and graph analysis and monetization-- all the themes that have made it a great learning experience for me.