Real-time Auction Research Articles

Convexity in Real-time Bidding and Related Problems

We study problems arising in real-time auction markets, common in e-commerce and computational advertising, where bidders face the problem of calculating optimal bids. We focus upon a contract management problem where a demand aggregator is subject to multiple contractual obligations requiring them to acquire items of heterogeneous types at a specified rate and where they will seek to fulfill these obligations at minimum cost. Our main results show that, through a transformation of variables, this problem can be formulated as a convex optimization problem, for both first and second price auctions. The resulting duality theory admits rich structure and interpretations. Additionally, we show that the transformation of variables can be used to guarantee the convexity of optimal bidding problems studied by other authors, who did not leverage convexity in their analysis. The main point of our work is to emphasize that the natural convexity properties arising in first and second price auctions are not being fully exploited. Finally, we show direct analogies to problems in financial markets: the expected cost of bidding in second price auctions is formally identical to certain transaction costs when submitting market orders, and that a related conjugate function arises in dark pool financial markets.

Peer-to-peer electricity trading model for urban virtual power plants considering prosumer preferences and power demand heterogeneity

Peer-to-peer electricity trading has emerged as a critical way to meet the varied demands of prosumers within urban virtual power plants. To better support supply-demand balance and ensure the stable, low-carbon and efficient peer-to-peer electricity trading, both prosumer trading preferences and power demand heterogeneity need to be considered. This study proposes a peer-to-peer electricity trading model for urban virtual power plants that considers both preference of prosumers and power demand heterogeneity. First, to effectively meet the trading demands of diverse prosumers and enhance the penetration of renewable energy, prosumers are classified based on variations in financial returns, green electricity preferences and risk avoidance. Power demand is further classified based on the trading preferences of prosumers. Then, a peer-to-peer electricity trading model incorporating a game model and a market bidding mechanism is proposed to enable efficient and dynamic matching between supply and demand. Finally, to mitigate the impact of renewable energy uncertainty on peer-to-peer electricity trading, real-time auctions are implemented across multiple virtual power plants. The results show that the proposed model is capable of more effectively accommodating the diverse demands of prosumers, and facilitates a more stable, low-carbon and efficient peer-to-peer electricity trading.

Fixed Point Label Attribution for Real-Time Bidding

Problem definition: Most of the display advertising inventory is sold through real-time auctions. The participants of these auctions are typically bidders (Google, Criteo, RTB House, and Trade Desk for instance) that participate on behalf of advertisers. In order to estimate the value of each display opportunity, they usually train advanced machine learning algorithms using historical data. In the labeled training set, the inputs are vectors of features representing each display opportunity, and the labels are the generated rewards. In practice, the rewards are given by the advertiser and are tied to whether a particular user converts. Consequently, the rewards are aggregated at the user level and never observed at the display level. A fundamental task that has, to the best of our knowledge, been overlooked is to account for this mismatch and split, or attribute, the rewards at the right granularity level before training a learning algorithm. We call this the label attribution problem. Methodology/results: In this paper, we develop an approach to the label attribution problem, which is both theoretically justified and practical. In particular, we develop a fixed point algorithm that allows for large-scale implementation and showcase our solution using a large-scale publicly available data set from Criteo, a large demand-side platform. We dub our approach the fixed point label attribution algorithm. Managerial implications: There is often a hidden leap of faith when transforming the advertiser’s signal into display labeling. Demand Side Platforms providers should be careful when building their machine learning pipeline and carefully solve the label attribution step.

An Auction-Based Approach for Multi-Agent Uniform Parallel Machine Scheduling with Dynamic Jobs Arrival

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents. All agents are self-interested and rational with the aim of maximizing their own objectives, resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information. Within the context, a centralized scheduling approach is unfeasible, and a decentralized approach is considered to deal with the targeted problem. This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’ preferences under incomplete information. For this purpose, a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed. In the proposed approach, a dynamic auction procedure is established for dynamic jobs participating in a real-time auction, and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination. In addition, an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently. A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids. Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on large-scale problems with numerous consumer agents and jobs. A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

Real Time Auction Models for Programmatic Advertising Efficiency

The rapid evolution of programmatic advertising has necessitated the development of real-time auction models to enhance efficiency and optimize ad spend. This study explores the dynamics of real-time auctions within programmatic advertising, focusing on the mechanisms that drive bid strategies and pricing models. By analyzing the interplay between advertisers, publishers, and demand-side platforms (DSPs), we identify the factors influencing bidding behavior and auction outcomes. We propose a novel framework that integrates machine learning algorithms to predict bid values based on historical data and contextual parameters, aiming to improve decision-making processes in real-time environments. Furthermore, this research examines the impact of auction transparency and competition on advertising effectiveness, revealing how different auction formats—such as second-price and first-price auctions—affect bidder strategies and overall campaign performance. We employ empirical analysis using data from various programmatic platforms to validate our model, demonstrating significant improvements in cost efficiency and ad placement outcomes. Ultimately, our findings contribute to a deeper understanding of real-time auction dynamics in programmatic advertising, providing actionable insights for marketers seeking to enhance campaign efficiency. By leveraging advanced analytics and real-time data, advertisers can better navigate the complexities of programmatic ecosystems, resulting in optimized advertising strategies that maximize return on investment (ROI) while effectively engaging target audiences.

Best of Both Worlds Ad Contracts: Guaranteed Allocation and Price with Programmatic Efficiency

Buying display ad impressions via real-time auctions comes with significant allocation and price uncertainties. We design and analyze a contract that mitigates this uncertainty risk by providing guaranteed allocation and prices while maintaining the efficiency of buying in an auction. We study how risk aversion affects the desire for guarantees and how to price a guaranteed allocation. We propose to augment the traditional auction with a programmatic purchase option (which we call a Market-Maker contract) that removes allocation and price uncertainties. Instead of participating in the auction, advertisers can secure impressions in advance at a fixed premium price offered by the Market-Maker. It is then the responsibility of the Market-Maker to procure these impressions by bidding in the auction. We model buyers as risk-averse agents and analyze the equilibrium outcome when buyers face two purchase options (auction and Market-Maker contract). We derive analytical expressions for the Market-Maker price that reveal insightful relationships with uncertainties in the auction price and buyers’ risk levels. We also show the existence of a Market-Maker price that simultaneously improves the seller’s revenue and the sum of buyers’ utilities. As a building block to our analysis, we establish the truthfulness of the multiunit auction when buyers have nonquasilinear utilities because of risk aversion. Recently, the Google’s Display & Video 360 platform started offering a product akin to Market-Maker called “Guaranteed Packages,” which was inspired by this paper. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Predictive Analytics for Real-time Auction Bidding Support: a Case on Fantasy Football

This work reports about an end-to-end business analytics experiment, applying predictive and prescriptive analytics to real-time bidding support for fantasy football draft auctions. Forecast methods are used to quantify the expected return of each investment alternative, while subgradient optimization is used to provide adaptive online recommendations on the allocation of scarce budget resources. A distributed front-end implementation of the prescriptive modules and the rankings of simulated leagues testify the viability of this architecture for actual support.

Context Information Can Increase Revenue in Online Display Advertising Auctions: Evidence from a Policy Change

Ad exchanges, where real-time auctions for display ad impressions take place, have historically emphasized user targeting, and advertisers sometimes did not know on which sites their ads would appear; in other words, they had no ad context. More recently, some ad exchanges have been encouraging publishers to provide context information to ad buyers that would allow them to adjust their bids for ads at specific sites. This article explores the empirical effect of a change in context information provided by a private European ad exchange. Analyzing this as a quasi-experiment using difference in differences, the authors find that average revenue per impression rose when the exchange provided subdomain information to ad buyers. Thus, ad context information is important to ad buyers, and they will act on it. Revenue per impression rises for nearly all sites, which is what auction theory predicts will happen when rational buyers with heterogeneous preferences are given more information. Exceptions are sites with thin markets prior to the policy change; consistent with theory, these sites do not show a rise in prices. This study adds evidence that ad exchanges with reputable publishers, particularly smaller volume, high-quality sites, should provide ad buyers with context information, which can be done at almost no cost.

Real-time Bidding for Time Constrained Impression Contracts in First and Second Price Auctions - Theory and Algorithms

We study the optimal bids and allocations in a real-time auction for heterogeneous items subject to the requirement that specified collections of items of given types be acquired within given time constraints. The problem is cast as a continuous time optimization problem that can, under certain weak assumptions, be reduced to a convex optimization problem. Focusing on the standard first and second price auctions, we first show, using convex duality, that the optimal (infinite dimensional) bidding policy can be represented by a single finite vector of so-called ''pseudo-bids''. Using this result we are able to show that the optimal solution in the second price case turns out to be a very simple piecewise constant function of time. This contrasts with the first price case that is more complicated. Despite the fact that the optimal solution for the first price auction is genuinely dynamic, we show that there remains a close connection between the two cases and that, empirically, there is almost no difference between optimal behavior in either setting. This suggests that it is adequate to bid in a first price auction as if it were in fact second price. Finally, we detail methods for implementing our bidding policies in practice with further numerical simulations illustrating the performance.

Multi-armed bandits for bid shading in first-price real-time bidding auctions

Online advertisements are bought through a mechanism called real-time bidding (RTB). In RTB, the ads are auctioned in real-time on every webpage load. The ad auctions can be of two types: second-price or first-price auctions. In second-price auctions, the bidder with the highest bid wins the auction, but they only pay the second-highest bid. This paper focuses on first-price auctions, where the buyer pays the amount that they bid. This research evaluates how multi-armed bandit strategies optimize the bid size in a commercial demand-side platform (DSP) that buys inventory through ad exchanges. First, we analyze seven multi-armed bandit algorithms on two different offline real datasets gathered from real second-price auctions. Then, we test and compare the performance of three algorithms in a production environment. Our results show that real data from second-price auctions can be used successfully to model first-price auctions. Moreover, we found that the trained multi-armed bandit algorithms reduce the bidding costs considerably compared to the baseline (naïve approach) on average 29%and optimize the whole budget by slightly reducing the win rate (on average 7.7%). Our findings, tested in a real scenario, show a clear and substantial economic benefit for ad buyers using DSPs.

Dynamically Optimizing Display Advertising Profits under Diverse Budget Settings

As a revolutionary auction mechanism for display advertising, real-time bidding (RTB) allows advertisers to purchase individual ad impressions through real-time auctions. In RTB, the demand-side platform (DSP) acts as advertisers' bidding agent and aims at developing appropriate bidding strategies to maximize their specific key performance indicators (KPIs). Existing bidding strategies perform well for optimizing profits when the ad budget severely limited. However, when there is sufficient budget, their performance deteriorates. This results in added complexity for advertisers when applying these approaches in practice, hindering wider adoption. To address this challenging limitation, we propose the Adaptive ROI-Aware Bidding (ARAB) approach. It intelligently analyzes the budget setting and auction market conditions, and adjusts the bidding function accordingly to optimize profits. Different from previous studies that only bid based on the ad revenue, our proposed ROI-aware bidding function also takes into account the ad cost at impression-level. By doing so, ARAB dynamically allocates the budget on more cost-effective impressions to increase profits. Through extensive offline experiments on two real-world public datasets, we demonstrate that the proposed ARAB has achieved significant improvements in terms of both profit and ROI compared to state-of-the-art approaches.

Placement Disclosure in Ad Auctions: Evidence From a Policy Change

Ad exchanges, i.e., platforms where real-time auctions for ad impressions take place, have developed sophisticated technology and data ecosystems to allow advertisers to target users, yet advertisers may not know which sites their ads appear on, i.e., the ad context. In practice, ad exchanges can require publishers to provide accurate ad placement information to ad buyers prior to submitting their bids, allowing them to adjust their bids for ads at specific domains, subdomains or URLs. However, ad exchanges have historically been reluctant to disclose placement information due to fears that buyers will start buying ads only on the most desirable sites leaving inventory on other sites unsold and lowering average revenue. This paper explores the empirical effect of ad placement disclosure using a unique data set describing a change in context information provided by a major private European ad exchange. Analyzing this as a quasi-experiment using diff-in-diff, we find that average revenue per impression rose when more context information was provided. This shows that ad context information is important to ad buyers and that providing more context information will not lead to deconflation. The exception to this are sites which had a low number of buyers prior to the policy change; consistent with theory, these sites with thin markets do not show a rise in prices. Our analysis adds evidence that ad exchanges with reputable publishers, particularly smaller volume, high quality sites, should provide ad buyers with site placement information, which can be done at almost no cost.

Fast Core Pricing for Rich Advertising Auctions

Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities, where truthful auctions such as the Vickrey-Clarke-Groves (VCG) can yield unacceptably low revenue. We therefore study core selecting auctions, which boost revenue by setting payments so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different outcome. Our main result is a combinatorial algorithm that finds an approximate bidder optimal core point with almost linear number of calls to the welfare maximization oracle. Our algorithm is faster than previously-proposed heuristics in the literature and has theoretical guarantees. We conclude that core pricing is implementable even for very time sensitive practical use cases such as realtime auctions for online advertising and can yield more revenue. We justify this claim experimentally using the Microsoft Bing Ad Auction data, through which we show our core pricing algorithm generates almost 26% more revenue than VCG on average, about 9% more revenue than other core pricing rules known in the literature, and almost matches the revenue of the standard Generalized Second Price (GSP) auction.

Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand

While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present a dynamic programming model to study how an online publisher should optimally allocate and price page views between guaranteed and spot markets. The problem is challenging because the allocation and pricing of guaranteed contracts affect how advertisers split their purchases between the two markets, and the terminal value of the model is endogenously determined by the updated dual force of supply and demand in auctions. We take the advertisers’ purchasing behaviour into consideration, i.e., risk aversion and stochastic demand arrivals, and present a scalable and efficient algorithm for the optimal solution. The model is also empirically validated with a commercial dataset. The experimental results show that selling page views via both channels can increase the publisher’s expected total revenue, and the optimal pricing and allocation strategies are robust to different market and advertiser types.

Learning Optimal Online Advertising Portfolios with Periodic Budgets

Online advertising enables advertisers to reach customers with personalized ads. Advertisers need to determine the right targets for their ads and how much they are willing to pay to engage those targets. A large portion of online ads are priced using real-time auctions, thus advertisers need to decide which targets to bid on in these auctions. Collaborating with one of the largest ad-tech firms in the world, we develop new algorithms that help advertisers bid optimally on target portfolios while taking into account some limitations inherent to online advertising. We study this problem as a Multi-Armed Bandit (MAB) problem with periodic budgets. At the beginning of each time period, the advertiser needs to determine which portfolio of target to select to maximize the expected total revenue (revenue from clicks/conversions), while maintaining the total cost of auction payments within the advertising budget. In this paper, we formulate the problem and develop an Optimistic-Robust Learning (ORL) algorithm that uses ideas from Upper Confidence Bound (UCB) algorithms and robust optimization. We prove that the expected cumulative regret of the algorithm is bounded. Additionally, simulations on synthetic and real-world data show that the ORL algorithm reduces regret by at least 10-20% compared to benchmarks.

An auction-based incentive mechanism for heterogeneous mobile clouds

Traditional mobile cloud computing (MCC) adopts a onefold mobile device and public cloud paradigm. As the mobile device capabilities continue to develop, their opportunistic utilization in MCC has recently gained popularity. The new paradigm is a hybrid/heterogeneous mobile cloud (HMC) where mobile devices, cloudlets, and private/public cloud form a shared resource network for task offloading. However, mobile device users are discouraged from sharing their devices for running foreign tasks due to the battery life and privacy concerns. To incentivize mobile device users to utilize and participate in the HMC offloading service, we designed a task offloading market for the HMC service, where a mobile user can compete as a seller with others by bidding its redundant computing resources, and another mobile user as a buyer can pay the bidding price and offload the task to the winning user. To enable an incentive and fair competition of the mobile cloud offloading market, we propose a reverse auction-based incentive mechanism, mCloudAuc, to provide real-time auctions. The proposed auction algorithm demonstrates computation efficiency, truthfulness, and individual rationality for the participants through proof and multiple simulations. Our prototype implementation of mCloudAuc on Android platform has also shown its feasibility in practice.

A New Approach to Real-Time Bidding in Online Advertisements: Auto Pricing Strategy

Real-time bidding (RTB) for digital advertising is becoming the norm for improving advertisers’ campaigns. Unlike traditional advertising practices, in the process of RTB, the advertisement slots of a mobile application or a website are mapped to a particular advertiser through a real-time auction. The auction is triggered and is held for a few milliseconds after an application is launched. As one of the key components of the RTB ecosystem, the demand-side platform gives the advertisers a full pledge window to bid for available impressions. Because of the fast-growing market of mobile applications and websites, the selection of the most pertinent target audience for a particular advertiser is not a simple human-mediated process. The real-time programmatic approach has become popular instead. To address the complexity and dynamic nature of the RTB process, we propose an auto pricing strategy (APS) approach to determine the applications to bid for and their respective bid prices from the advertising agencies’ perspective. We apply the APS to actual RTB data and demonstrate how it outperforms the existing RTB approaches with a higher conversion rate for a lower target spend. A video abstract is available at https://doi.org/10.1287/ijoc.2018.0812 .

Pricing Average Price Advertising Options When Underlying Spot Market Prices Are Discontinuous

Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.

Understanding programmatic TV advertising

ABSTRACTTelevision is undergoing tremendous technological developments, which will enable marketers to direct commercial messages to more specific audiences at the individual and/or household level. Traditional ways of buying TV advertising are being challenged by the programmatic approach, which originated with search and display ads, and uses data and technology and real-time auctions to automate transactions between buyers and sellers. We discuss the changes to TV that enable the rise of programmatic models and propose that its future success depends on the coordination and availability of three factors, abbreviated DAD: distribution, ad inventory and data. We also elaborate on the effects of media context and how the TV program environment matters for advertising effectiveness. Finally, we discuss the future of TV advertising and related research needs.

The Value of Consumer Data in Online Advertising

Abstract In this paper we propose a model where consumer personal data have multidimensional characteristics, and are used by platforms to offer ad slots with better targeting possibilities to a market of differentiated advertisers through real-time auctions. A platform controls the amount of information about consumers that it discloses to advertisers, thereby affecting the dispersion of advertisers’ valuations for the slot. We first show by way of simulations that the amount of consumer-specific information that is optimally revealed to advertisers increases with the degree of competition on the advertising market and decreases with the cost of information disclosure for a monopolistic platform, competing platforms or a welfare-maximizing platform, provided the advertising market is not highly concentrated. Second, we exhibit different properties between the welfare-maximizing situation and the imperfectly competitive market situations with respect to how the incremental value of information varies: there are decreasing social returns to consumers’ data, while private returns may be increasing or decreasing locally.