# Reframing Audience Expansion through the Lens of Probability Density Estimation

arXiv: https://arxiv.org/abs/2311.05853 · HTML: https://mlsanity.com/paper/2311.05853/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising](https://mlsanity.com/paper/2105.14688/) (2021)
- [Real-time Attention Based Look-alike Model for Recommender System](https://mlsanity.com/paper/1906.05022/) (2019)

## Abstract (arXiv)

Audience expansion has become an important element of prospective marketing, helping marketers create target audiences based on a mere representative sample of their current customer base. Within the realm of machine learning, a favored algorithm for scaling this sample into a broader audience hinges on a binary classification task, with class probability estimates playing a crucial role. In this paper, we review this technique and introduce a key change in how we choose training examples to ensure the quality of the generated audience. We present a simulation study based on the widely used MNIST dataset, where consistent high precision and recall values demonstrate our approach's ability to identify the most relevant users for an expanded audience. Our results are easily reproducible and a Python implementation is openly available on GitHub: urlhttps://github.com/carvalhaes-ai/audience-expansion