A novel auction system for selecting advertisements in Real-Time bidding
- URL: http://arxiv.org/abs/2010.11981v1
- Date: Thu, 22 Oct 2020 18:36:41 GMT
- Title: A novel auction system for selecting advertisements in Real-Time bidding
- Authors: Luis Miralles-Pechu\'an and Fernando Jim\'enez and Jos\'e Manuel
Garc\'ia
- Abstract summary: 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.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Real-Time Bidding is a new Internet advertising system that has become very
popular in recent years. This system works like a global auction where
advertisers bid to display their impressions in the publishers' ad slots. The
most popular system to select which advertiser wins each auction is the
Generalized second-price auction in which the advertiser that offers the most
wins the bet and is charged with the price of the second largest bet. In this
paper, 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. The factors that we consider are, among
others, the benefit that can be given to each advertiser, the probability of
conversion from the advertisement, the probability that the visit is
fraudulent, how balanced are the networks participating in RTB and if the
advertisers are not paying over the market price. In addition, we propose a
methodology based on genetic algorithms to optimize the selection of each
advertiser. We also conducted some experiments to compare the performance of
the proposed model with the famous Generalized Second-Price method. We think
that this new approach, which considers more relevant aspects besides the
price, offers greater benefits for RTB networks in the medium and long-term.
Related papers
- 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) - 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) - Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions [42.002983450368134]
We study the question of how to bid in first-price auctions.
Unlike in second-price auctions, bidding one's private value truthfully is no longer optimal.
We consider two types of hints: one where a single point-prediction is available, and the other where a hint interval is available.
arXiv Detail & Related papers (2022-11-05T19:20:53Z) - An Analysis of Selection Bias Issue for Online Advertising [0.30458514384586394]
We show a selection bias issue that is present in an auction system.
We analyze that the selection bias destroy truthfulness of the auction.
Experiment shows that the selection bias is drastically reduced by using a multi-task learning which learns the data for all advertisements.
arXiv Detail & Related papers (2022-06-07T12:29:40Z) - 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) - 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) - Optimal Allocation of Real-Time-Bidding and Direct Campaigns [10.888918892489638]
We consider the problem of optimizing the revenue a web publisher gets through real-time bidding (i.e. from ads sold in real-time auctions) and direct (i.e. from ads sold through contracts agreed in advance)
This paper presents an algorithm to build an optimal strategy for the publisher to deliver its direct campaigns while maximizing its real-time bidding revenue.
arXiv Detail & Related papers (2020-06-12T10:44:56Z) - Online Joint Bid/Daily Budget Optimization of Internet Advertising
Campaigns [115.96295568115251]
We study the problem of automating the online joint bid/daily budget optimization of pay-per-click advertising campaigns over multiple channels.
For every campaign, we capture the dependency of the number of clicks on the bid and daily budget by Gaussian Processes.
We design four algorithms and show that they suffer from a regret that is upper bounded with high probability as O(sqrtT)
We present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1,000 Euros for more than one year.
arXiv Detail & Related papers (2020-03-03T11:07:38Z) - Online Causal Inference for Advertising in Real-Time Bidding Auctions [1.9336815376402723]
This paper proposes a new approach to perform causal inference on advertising bought through real-time bidding systems.
We first show that the effects of advertising are identified by the optimal bids.
We introduce an adapted Thompson sampling (TS) algorithm to solve a multi-armed bandit problem.
arXiv Detail & Related papers (2019-08-22T21:13:03Z)
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.