# Display advertising: Estimating conversion probability efficiently

arXiv: https://arxiv.org/abs/1710.08583 · HTML: https://mlsanity.com/paper/1710.08583/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [Handling many conversions per click in modeling delayed feedback](https://mlsanity.com/paper/2101.02284/) (2021)
- [Multi-Head Online Learning for Delayed Feedback Modeling](https://mlsanity.com/paper/2205.12406/) (2022)
- [Learning to Allocate Resources with Censored Feedback](https://mlsanity.com/paper/2602.06565/) (2026)

## Abstract (arXiv)

The goal of online display advertising is to entice users to "convert" (i.e., take a pre-defined action such as making a purchase) after clicking on the ad. An important measure of the value of an ad is the probability of conversion. The focus of this paper is the development of a computationally efficient, accurate, and precise estimator of conversion probability. The challenges associated with this estimation problem are the delays in observing conversions and the size of the data set (both number of observations and number of predictors). Two models have previously been considered as a basis for estimation: A logistic regression model and a joint model for observed conversion statuses and delay times. Fitting the former is simple, but ignoring the delays in conversion leads to an under-estimate of conversion probability. On the other hand, the latter is less biased but computationally expensive to fit. Our proposed estimator is a compromise between these two estimators. We apply our results to a data set from Criteo, a commerce marketing company that personalizes online display advertisements for users.