# Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising

arXiv: https://arxiv.org/abs/2304.00999 · HTML: https://mlsanity.com/paper/2304.00999/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 21 of 21)

- [Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits](https://mlsanity.com/paper/1807.07623/) (2018)
- [Explore no more: Improved high-probability regret bounds for non-stochastic bandits](https://mlsanity.com/paper/1506.03271/) (2015)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [What Doubling Tricks Can and Can't Do for Multi-Armed Bandits](https://mlsanity.com/paper/1803.06971/) (2018)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Stochastic Bandit Models for Delayed Conversions](https://mlsanity.com/paper/1706.09186/) (2017)
- [Online learning in repeated auctions](https://mlsanity.com/paper/1511.05720/) (2015)
- [Learning to Bid Without Knowing your Value](https://mlsanity.com/paper/1711.01333/) (2017)
- [Optimal No-regret Learning in Repeated First-price Auctions](https://mlsanity.com/paper/2003.09795/) (2020)
- [Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions](https://mlsanity.com/paper/2007.04568/) (2020)
- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising](https://mlsanity.com/paper/2106.06224/) (2021)
- [Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising](https://mlsanity.com/paper/2012.02930/) (2020)
- [Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions](https://mlsanity.com/paper/2211.06358/) (2022)
- [Bidding Agent Design in the LinkedIn Ad Marketplace](https://mlsanity.com/paper/2202.12472/) (2022)
- [Learning Valuation Distributions from Partial Observation](https://mlsanity.com/paper/1407.2855/) (2014)
- [Analysis of Dual-Based PID Controllers through Convolutional Mirror Descent](https://mlsanity.com/paper/2202.06152/) (2022)
- [The Impact of Batch Learning in Stochastic Linear Bandits](https://mlsanity.com/paper/2202.06657/) (2022)
- [Prior-independent Auctions for Risk-averse Agents](https://mlsanity.com/paper/1301.0401/) (2013)

## Abstract (arXiv)

This paper presents a bidding system for sponsored search auctions under an unknown valuation model. This formulation assumes that the bidder's value is unknown, evolving arbitrarily, and observed only upon winning an auction. Unlike previous studies, we do not impose any assumptions on the nature of feedback and consider the problem of bidding in sponsored search auctions in its full generality. Our system is based on a bandit framework that is resilient to the black-box auction structure and delayed and batched feedback. To validate our proposed solution, we conducted a case study at Zalando, a leading fashion e-commerce company. We outline the development process and describe the promising outcomes of our bandits-based approach to increase profitability in sponsored search auctions. We discuss in detail the technical challenges that were overcome during the implementation, shedding light on the mechanisms that led to increased profitability.