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Ads are often designed visually, with images and videos conveying information. In this work, we study the accessibility of ads on the web to users of screen readers. We approach this in two ways: first, we conducted a measurement and analysis of 90 ...

Graph-based Recommendation Systems (GRSs) have gained prominence for their ability to enhance the accuracy and effectiveness of recommender systems by exploiting structural relationships in user-item interaction data. Despite their advanced capabilities, ...

Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mismatches between predicted probabilities and true likelihoods due to the ...

In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opportunities that have arisen as markets embrace this autobidding and cover ...

Online advertising platforms leverage a two-stage auction architecture to deliver personalized ads to users with low latency. The first stage efficiently selects a small subset of promising candidates out of the complete pool of ads. In the second stage, ...

In recent years, Optimized Cost Per Click (OCPC) and Optimized Cost Per Mille (OCPM) have emerged as the most widely adopted pricing models in the online advertising industry. However, the existing literature has yet to identify the specific conditions ...

This paper presents GATS (<u>G</u>enerative <u>A</u>udience <u>T</u>argeting <u>S</u> ystem for Online Advertising), a new framework using large language models (LLMs) to improve audience targeting in online advertising. GATS overcomes the shortcomings ...

Click-through rate (CTR) prediction plays an important role in online advertising platforms. Most existing methods use data from the advertising platform itself for CTR prediction. As user behaviors also exist on many other platforms, e.g., media ...

As an emerging privacy-preserving approach to leveraging cross-platform user interactions, vertical federated learning (VFL) has been increasingly applied in recommender systems. However, vanilla VFL is only applicable to overlapped users, ignoring ...

Advertising funds a number of services that play a major role in our everyday online experiences, from social networking, to maps, search, and news. As the power and reach of advertising platforms grow, so do the concerns about the potential for ...

Providing online content monetized via ads to users is a lucrative business. But what if the content is pirated or illicit, thus harming the brand safety of the advertiser? In this paper, we are the first to investigate Ad Laundering: a technique with ...

Online advertising plays a pivotal role in sustaining the accessibility of free content on the Internet, serving as a primary revenue source for websites and online services. This dynamic marketplace sees advertisers allocating budgets and competing for ...

In online platforms like eBay, sponsored search advertising has become instrumental for businesses aiming for enhanced visibility. However, in automated ad auctions, the sellers (ad campaigns) run the risk of exhausting their budgets prematurely in the ...

Effective user representations are pivotal in personalized advertising. However, stringent constraints on training throughput, serving latency, and memory, often limit the complexity and input feature set of online ads ranking models. This challenge is ...

Online advertising contributes a considerable part of the tech sector's revenue, and has been remarkably influencing the public agenda. With evolving developments, AI is playing an increasingly significant role in online advertising. We propose to create ...

Online advertising channels commonly focus on maximizing total advertiser welfare to enhance channel health, and previous literature has studied augmenting ad auctions with machine learning predictions on advertiser values (also known asmachine-learned ...

In recent years, the growing adoption of autobidding has motivated the study of auction design with value-maximizing auto-bidders. It is known that under mild assumptions, uniform bid-scaling is an optimal bidding strategy in truthful auctions, e.g., ...

On typical e-commerce platforms, a product can be displayed to users in two possible forms, as an ad item or an organic item. Usually, ad and organic items are separately selected by the advertising system and recommendation system, and then combined by ...

We investigate auction mechanisms to support the emerging format of AI-generated content. We in particular study how to aggregate several LLMs in an incentive compatible manner. In this problem, the preferences of each agent over stochastically generated ...

We study the price of anarchy of the generalized second-price auction where bidders are value maximizers (i.e., autobidders). We show that in general the price of anarchy can be as bad as 0. For comparison, the price of anarchy of running VCG is 1/2 in ...