A trio of patent applications from Google look at estimating the likelihood that an advertisement is a good one, in a method that goes beyond counting click-through-rates (CTR).
They provide an extremely detailed list of factors that might go into a quality score, as well as details of different statistical models that might be generated from gathering information about those different factors.
A system provides one or more advertisements to users in response to search queries and logs user behavior associated with user selection of the one or more advertisements. The system also logs features associated with selected ones of the one or more advertisements, or associated with the search queries.
The system further uses a statistical model and the logged user behavior to estimate quality scores associated with the selected advertisements and aggregates the estimated quality scores. The system predicts the quality of another advertisement using the aggregated quality scores.
A system obtains ratings associated with a first set of advertisements hosted by one or more servers, where the ratings indicate a quality of the first set of advertisements.
The system observes multiple different first user actions associated with user selection of advertisements of the first set of advertisements and derives a statistical model using the observed first user actions and the obtained ratings.
The system further observes second user actions associated with user selection of a second advertisement hosted by the one or more servers and uses the statistical model and the second user actions to estimate a quality of the second advertisement.
This third patent application discusses the comparions of advertisements and the different quality parameters associated with them, to determine whether ads should be filtered, where they should be ranked, and whether some should be promoted.
A system obtains a first parameter (QP.sub.1) associated with a quality of an advertisement among multiple advertisements, where the first quality parameter (QP.sub.1) does not include a click through rate (CTR).
The system functionally combines the first quality parameter (QP.sub.1) with at least one other parameter and uses the functional combination to filter, rank or promote the advertisement among the multiple advertisements.
The patent applications lists examples of 44 different factors that might be used in a quality score that doesn’t focus upon click through rates. These include such things as:
If you use paid advertisements through Google, these patent applications may be worth delving deeper into. It is pretty interesting to see all of the user behavior considerations that may go into determining a score and placement for an ad.
The documents all end by noting that conversion tracking may also be optionally used to see if a “direct calibration between predictive values and user satisfaction” can be derived.