Real-Time Optimization Of Web Publisher RTB Revenues
- URL: http://arxiv.org/abs/2006.07083v1
- Date: Fri, 12 Jun 2020 11:14:56 GMT
- Title: Real-Time Optimization Of Web Publisher RTB Revenues
- Authors: Pedro Chahuara, Nicolas Grislain, Gr\'egoire Jauvion and Jean-Michel
Renders
- Abstract summary: This paper describes an engine to optimize web publisher revenues from second-price auctions.
The engine is able to predict, for each auction, an optimal reserve price in approximately one millisecond.
- Score: 10.908037452134302
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper describes an engine to optimize web publisher revenues from
second-price auctions. These auctions are widely used to sell online ad spaces
in a mechanism called real-time bidding (RTB). Optimization within these
auctions is crucial for web publishers, because setting appropriate reserve
prices can significantly increase revenue. We consider a practical real-world
setting where the only available information before an auction occurs consists
of a user identifier and an ad placement identifier. The real-world challenges
we had to tackle consist mainly of tracking the dependencies on both the user
and placement in an highly non-stationary environment and of dealing with
censored bid observations. These challenges led us to make the following design
choices: (i) we adopted a relatively simple non-parametric regression model of
auction revenue based on an incremental time-weighted matrix factorization
which implicitly builds adaptive users' and placements' profiles; (ii) we
jointly used a non-parametric model to estimate the first and second bids'
distribution when they are censored, based on an on-line extension of the
Aalen's Additive model.
Our engine is a component of a deployed system handling hundreds of web
publishers across the world, serving billions of ads a day to hundreds of
millions of visitors. The engine is able to predict, for each auction, an
optimal reserve price in approximately one millisecond and yields a significant
revenue increase for the web publishers.
Related papers
- A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
We address the problem of dynamically pricing complementary items that are sequentially displayed to customers.
Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective.
We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
arXiv Detail & Related papers (2024-07-08T09:55:31Z) - Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach [85.65587846913793]
Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles.
We propose an attribute-aware auction-based mechanism to optimize resource allocation during VTs migration.
We train a DDA auctioneer using a generative pre-trained transformer (GPT)-based deep reinforcement learning (DRL) algorithm to adjust the auction clocks efficiently.
arXiv Detail & Related papers (2024-06-08T09:41:38Z) - Advancing Ad Auction Realism: Practical Insights & Modeling Implications [2.8413290300628313]
This paper shows that one can still gain useful insight into modern ad auctions by modeling advertisers as agents governed by an adversarial bandit algorithm.
We find that soft floors yield lower revenues than suitably chosen reserve prices, even restricting attention to a single query.
arXiv Detail & Related papers (2023-07-21T17:45:28Z) - User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization [13.868805489082701]
We propose a new Markov Decision Process model for ad auctions to capture the user response to the quality of ads.
By incorporating user response, our model takes into consideration all three parties involved in the auction (advertiser, auctioneer, and user)
arXiv Detail & Related papers (2023-02-16T06:16:01Z) - Demystifying Advertising Campaign Bid Recommendation: A Constraint
target CPA Goal Optimization [19.857681941728597]
This paper presents a bid optimization scenario to achieve the desired cost-per-acquisition (tCPA) goals for advertisers.
We build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem.
The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors.
arXiv Detail & Related papers (2022-12-26T07:43:26Z) - VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations.
Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data.
Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
arXiv Detail & Related papers (2022-05-31T17:45:30Z) - Bidding via Clustering Ads Intentions: an Efficient Search Engine
Marketing System for E-commerce [13.601308818833301]
This paper introduces the end-to-end structure of the bidding system for search engine marketing for Walmart e-commerce.
We exploit the natural language signals from the users' query and the contextual knowledge from the products to mitigate the sparsity issue.
We analyze the online and offline performances of our approach and discuss how we find it as a production-efficient solution.
arXiv Detail & Related papers (2021-06-24T00:12:07Z) - A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in
Online Advertising [53.636153252400945]
We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies.
Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
arXiv Detail & Related papers (2021-06-11T08:07:14Z) - 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) - 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.