A growing proportion of digital advertising slots is purchased through real time bidding auctions, which enables advertisers to impose highly specific criteria on which devices and opportunities to target. Employing sophisticated targeting criteria reliably increases the performance of an ad campaign, however too strict criteria will limit its scale. This raises the need to estimate the number of anticipated ad impressions at a given campaign performance level, thus enabling advertisers to tune the campaign's budget to an optimal performance-scale trade-off. In this paper, we provide a way to estimate campaign impressions given the campaign criteria. There are several challenges to this problem. First, the criteria contain logic to include and exclude combinations of audience segments, making the space of possible criteria exponentially large. Furthermore, it is difficult to validate predictions, because we wish to predict the number of impressions available without budget constraints, a situation we can rarely observe in practice. In our approach, we first treat the audience segment inclusion/exclusion criteria separately as a data compression problem, where we use MinHash "sketches" to estimate audience size. We then model the number of available impressions with a regularized linear regression in log space, using multiplier features motivated by the assumption that some components of the additional campaign criteria are conditionally independent. We construct a validation set by projecting observed RTB data (under real budget constraints) to get impression availability without budget constraints. Using this approach, our average prediction is a factor of 2.2 from the true impression availability, and the deployed product responds to user requests in well under a second, meeting both accuracy and latency requirements for decision making in the execution of advertising campaigns.

Data generated by observing the actions of web browsers across the internet is being used at an ever increasing rate for both building models and making decisions. In fact, a quarter of the industry-track papers for KDD in 2012 were based on data generated by online actions. The models, analytics and decisions they inform all stem from the assumption that observed data captures the intent of users. However, a large portion of these observed actions are not intentional, and are effectively polluting the models. Much of this observed activity is either generated by robots traversing the internet or the result of unintended actions of real users. These non-intentional actions observed in the web logs severely bias both analytics and the models created from the data. In this paper, we will show examples of how non-intentional traffic that is produced by fraudulent activities adversely affects both general analytics and predictive models, and propose an approach using co-visitation networks to identify sites that have large amounts of this fraudulent traffic. We will then show how this approach, along with a second stage classifier that identifies non-intentional traffic at the browser level, is deployed in production at Media6Degrees (m6d), a targeting technology company for display advertising. This deployed product acts both to filter out the fraudulent traffic from the input data and to insure that we don't serve ads during unintended website visits.

Billions of online display advertising spots are purchased on a daily basis through real time bidding exchanges (RTBs). Advertising companies bid for these spots on behalf of a company or brand in order to purchase these spots to display banner advertisements. These bidding decisions must be made in fractions of a second after the potential purchaser is informed of what location (Internet site) has a spot available and who would see the advertisement. The entire transaction must be completed in near real-time to avoid delays loading the page and maintain a good users experience. This paper presents a bid-optimization approach that is implemented in production at Media6Degrees for bidding on these advertising opportunities at an appropriate price. The approach combines several supervised learning algorithms, as well as second price auction theory, to determine the correct price to ensure that the right message is delivered to the right person, at the right time.

This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding good audiences for brand advertising (as opposed to click maximizing, for example). Targeting social-network neighbors resonates well with advertisers, and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously. Besides being one of the first papers to our knowledge on data mining for on-line brand advertising, this paper makes several important contributions. We introduce a framework for evaluating brand audiences, in analogy to predictive-modeling holdout evaluation. We introduce methods for extracting quasi-social networks from data on visitations to social networking pages, without collecting any information on the identities of the browsers or the content of the social-network pages. We introduce measures of brand proximity in the network, and show that audiences with high brand proximity indeed show substantially higher brand affinity. Finally, we provide evidence that the quasi-social network embeds a true social network, which along with results from social theory offers one explanation for the increase in brand affinity of the selected audiences.