Reframing Audience Expansion through the Lens of Probability Density Estimation

Audience expansion is a methodology developed by ad-serving platforms to help advertisers find the best-matched audiences for their ads without looking into audience specifics. The rationale is that if you advertise to people who are similar to ones who already like the product or service you want to sell, chances are the conversion rate will be high. By leveraging this methodology advertisers can effortlessly reach their ideal leads by simply uploading a list of reference individuals, also known as a seed audience, to the platform. Then, the platform expands this seed to an audience of the desired size, typically resulting in a significant reduction in customer acquisition costs compared to other targeting strategies. From a machine learning perspective, a sound strategy for expanding a seed audience is by framing the problem as a binary classification task [Qu et al., 2014, Shen et al., 2015, Liu et al., 2016, Ma et al., 2016b,a]. Essentially, this involves creating a two-class labeled training set, consisting of seed users and non-seed users, and then training a probabilistic classifier, e.g., Logistic Regression [Jiang et al., 2019], to distinguish between the two classes. But instead of generating class predictions, the goal is to estimate the conditional probability that a given user belongs to the positive class. This probability is used to prioritize users for the expanded audience.