Search and Score-Based Waterfall Auction Optimization
- URL: http://arxiv.org/abs/2201.06409v1
- Date: Mon, 17 Jan 2022 13:59:12 GMT
- Title: Search and Score-Based Waterfall Auction Optimization
- Authors: Dan Halbersberg, Matan Halevi, Moshe Salhov
- Abstract summary: We learn a waterfall strategy from historical data by wisely searching in the space of possible waterfalls and selecting the one leading to the highest revenue.
Our framework guarantees that the waterfall revenue improves between iterations until converging to a local optimum.
- Score: 0.7734726150561088
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Online advertising is a major source of income for many online companies. One
common approach is to sell online advertisements via waterfall auctions, where
a publisher makes sequential price offers to ad networks. The publisher
controls the order and prices of the waterfall and by that aims 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 revenue. 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 until converging to a local
optimum. Real-world demonstrations are provided to show that the proposed
method improves the total revenue of real-world waterfalls compared to manual
expert optimization. Finally, the code and the data are available here.
Related papers
- Procurement Auctions via Approximately Optimal Submodular Optimization [53.93943270902349]
We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs.
Our goal is to design computationally efficient auctions that maximize the difference between the quality of the acquired services and the total cost of the sellers.
arXiv Detail & Related papers (2024-11-20T18:06:55Z) - Fair Allocation in Dynamic Mechanism Design [57.66441610380448]
We consider a problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of $T$ rounds.
The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group.
arXiv Detail & Related papers (2024-05-31T19:26:05Z) - Coordinated Dynamic Bidding in Repeated Second-Price Auctions with
Budgets [17.937079224726073]
We study coordinated online bidding algorithms in repeated second-price auctions with budgets.
We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding.
arXiv Detail & Related papers (2023-06-13T11:55:04Z) - Incentive-Aware Recommender Systems in Two-Sided Markets [49.692453629365204]
We propose a novel recommender system that aligns with agents' incentives while achieving myopically optimal performance.
Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets.
Both algorithms satisfy an ex-post fairness criterion, which protects agents from over-exploitation.
arXiv Detail & Related papers (2022-11-23T22:20:12Z) - On Second-order Optimization Methods for Federated Learning [59.787198516188425]
We evaluate the performance of several second-order distributed methods with local steps in the federated learning setting.
We propose a novel variant that uses second-order local information for updates and a global line search to counteract the resulting local specificity.
arXiv Detail & Related papers (2021-09-06T12:04:08Z) - A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
Real-Time Bidding is a new Internet advertising system that has become very popular in recent years.
We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
arXiv Detail & Related papers (2020-10-22T18:36:41Z) - Optimizing AD Pruning of Sponsored Search with Reinforcement Learning [14.583308909225552]
Industrial sponsored search system (SSS) can be logically divided into three modules: keywords matching, ad retrieving, and ranking.
The problem we are going to address is: how to pick out the best $K$ items from $N$ candidates to maximize the system's revenue.
We propose a novel model-free reinforcement learning approach to fixing this problem.
arXiv Detail & Related papers (2020-08-05T09:19:10Z) - Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential
Advertising [52.3825928886714]
We formulate the sequential advertising strategy optimization as a dynamic knapsack problem.
We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space.
To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
arXiv Detail & Related papers (2020-06-29T18:50:35Z) - Reserve Price Optimization for First Price Auctions [14.18752189817994]
We propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders' responsiveness to experimental shocks in reserves.
We show that revenue in a first-price auction can be usefully decomposed into a emphdemand component and a emphbidding component, and introduce techniques to reduce the variance of each component.
arXiv Detail & Related papers (2020-06-11T15:35:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.