# Search and Score-based Waterfall Auction Optimization

arXiv: https://arxiv.org/abs/2201.06409 · HTML: https://mlsanity.com/paper/2201.06409/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 17 Jan 2022.

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

- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [Maximizing net income of the auction waterfall with an abort decision tree](https://mlsanity.com/paper/1809.01245/) (2018)
- [Waterfall Bandits: Learning to Sell Ads Online](https://mlsanity.com/paper/1904.09404/) (2019)

## Abstract (arXiv)

Online advertising is a major source of income for many online companies. One common approach is to sell online advertisements via waterfall auctions, through which a publisher makes sequential price offers to ad networks. The publisher controls the order and prices of the waterfall in an attempt to maximize his revenue. In this work, we propose a methodology to learn a waterfall strategy from historical data by wisely searching in the space of possible waterfalls and selecting the one leading to the highest revenues. The contribution of this work is twofold; First, we propose a novel method to estimate the valuation distribution of each user, with respect to each ad network. Second, we utilize the valuation matrix to score our candidate waterfalls as part of a procedure that iteratively searches in local neighborhoods. Our framework guarantees that the waterfall revenue improves between iterations ultimately converging into a local optimum. Real-world demonstrations are provided to show that the proposed method improves the total revenue of real-world waterfalls, as compared to manual expert optimization. Finally, the code and the data are available here.