Generative AI is being increasingly integrated into web search for the convenience it provides users. In this work, we aim to understand how generative AI disrupts web search by retrieving and presenting the information and sources differently from traditional search engines. We introduce a public benchmark dataset of 11,500 user queries to support our study and future research of generative search. We compare the search results returned by Google's search engine, the accompanying AI Overview (AIO), and Gemini Flash 2.5 for each query. We have made several key findings. First, we find that for 51.5% of representative, real-user queries, AIOs are generated, and are displayed above the organic search results. Controversial questions frequently result in an AIO. Second, we show that the retrieved sources are substantially different for each search engine (<0.2 average Jaccard similarity). Traditional Google search is significantly more likely to retrieve information from popular or institutional websites in government or education, while generative search engines are significantly more likely to retrieve Google-owned content. Third, we observe that websites that block Google's AI crawler are significantly less likely to be retrieved by AIOs, despite having access to the content. Finally, AIOs are less consistent when processing two runs of the same query, and are less robust to minor query edits. Our findings have important implications for understanding how generative search impacts website visibility, the effectiveness of generative engine optimization techniques, and the information users receive. We call for revenue frameworks to foster a sustainable and mutually beneficial ecosystem for publishers and generative search providers.

This paper presents a study of GDPR compliance under the Interactive Advertising Bureau Europe's Transparency and Consent Framework (TCF). This framework provides digital advertising market participants a standard for sharing users' privacy consent choices. TCF is widely used across the Internet, and this paper presents a thorough experimental evaluation of both the compliance of websites with TCF and its impact on user privacy. We reviewed 2,230 websites that use TCF and accepted the automatic decline of user consent by our data collection system. Unlike previous work on GDPR compliance, we found that most websites using TCF properly record the user's consent choice. However, we found that 72.8% of the websites that were TCF compliant claimed legitimate interest as a rationale for overriding the consent choice. While legitimate interest is legal under GDPR, previous studies have shown that most users disagreed with how it is being used to collect data. Additionally, analysis of cookies set to the browsers indicates that TCF may not fully protect user privacy even when websites are compliant. Our research provides regulators and publishers with a data collection and analysis system to monitor compliance, detect non-compliance, and examine questionable practices of circumventing user consent choices using legitimate interest.

Online publishers typically sell ad impressions through auctions held in ad exchanges in real-time, i.e., real-time bidding (RTB). A publisher will accept the winning bid if it is higher than a given reserve price for an ad impression. Setting an appropriate reserve price for an ad impression is critical for publishers’ revenue generation, but also challenging. While this problem has been studied for second-price auctions, it lacks studies for first-price auctions, the de facto industry standard since 2019. This paper proposes a machine learning model that determines the optimal reserve prices for individual ad impressions in real-time. It uses a multi-task learning framework to predict the lower bounds of the highest bids with a coverage probability, using only the data available to publishers. The experiments using data from a large international publisher show that the proposed model outperforms the comparison systems on generating revenue.

The fast increase in ad-blocker usage has resulted in significant revenue loss for online publishers. To mitigate this, many publishers implement the Wall strategy, where an adblock user is asked to whitelist the intended webpage. If the user refuses, the result is a loss-loss situation: the user is denied access to content, and the publisher cannot receive revenue. An alternative strategy, called AAX, is to show only acceptable ads to users. However, acceptable ads generate less revenue than regular ads. This article proposes personalized counter ad-blocking that dynamically chooses a counter ad-blocking strategy for individual users. To implement it, we propose a novel deep learning-based whitelist prediction model. Adblock users predicted to whitelist a page receive the Wall strategy; the others receive the AAX strategy. The proposed Deep Ad-Block Whitelist Network (DAWN) for whitelist prediction captures page characteristics, user interests in pages and their sensitivity to ads, reflected in historic behavior, using a deep learning mechanism. Furthermore, DAWN leverages multi-task learning on whitelist prediction and dwell-time prediction to boost performance. DAWN's effectiveness is validated on a real-world dataset provided by Forbes Media. The experimental results demonstrate the advantages of the proposed counter ad-blocking policy over existing policies on revenue generation and user engagement.

Fine-grained location prediction on smart phones can be used to improve app/system performance. Application scenarios include video quality adaptation as a function of the 5G network quality at predicted user locations, and augmented reality apps that speed up content rendering based on predicted user locations. Such use cases require prediction error in the same range as the GPS error, and no existing works on location prediction can achieve this level of accuracy. We propose Federated Meta-Location Learning (FMLL) on smart phones for fine-grained location prediction, based on GPSt races collected on the phones. FMLL has three components: a meta-location generation module, a prediction model, and a federated learning framework. The meta-location generation module represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. The model fuses Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system. FMLL uses federated learning to protect user privacy and reduce bandwidth consumption. Our experimental results, using a dataset with over 600,000 users, demonstrate that FMLL outperforms baseline models in terms of prediction accuracy. We also demonstrate that FMLL works well in conjunction with transfer learning, which enables model reusability. Finally, benchmark results on Android phones demonstrate FMLL’s feasibility in real life.

Traditional page recommendation models are endangered by stricter privacy regulations, such as the General Data Protection Regulation (GDPR). The performance of these models suffer when only a part of the users share their personal data, such as cookies, with web servers, while the rest of the users choose to opt-out from sharing these data. Furthermore, these models are not designed to provide recommendations for users who do not share their data. This paper addresses the question of how to provide good page recommendations to all users, independent of their privacy attitudes. We propose Fed4Rec, a privacy-preserving framework for page recommendation based on federated learning (FL) and model-agnostic meta-learning (MAML), which allows machine learning models to train on data collected from both public users, who share data with the server, and private users, who do not share data with the server. Fed4Rec enables recommendations for both public users, computed at the server, and private users, computed at their local devices. Private users' data are stored only on user devices and never shared with the server. FL is used to train on local data, and Fed4Rec shares with the server only partial model parameters, computed on local devices. MAML is used to jointly train on the public data and the model parameters from the private users. We compare Fed4Rec against several baseline frameworks, using a publicly available dataset from a large news portal. The results show that Fed4Rec outperforms the baselines in terms of recommendation accuracy. We also conduct one ablation study to examine the impact of varying the ratio between the number of public and private users. Fed4Rec performs better than the baselines for all ratios, but it is especially beneficial w hen t he p ercentage of public users is low.

The fast growing ad-blocker usage results in large revenue decrease for ad-supported online websites. Facing this problem, many online publishers choose either to cooperate with ad-blocker software companies to show acceptable ads or to build a wall that requires users to whitelist the site for content access. However, there is lack of studies on the impact of these two counter-ad-blocking strategies on user behaviors. To address this issue, we conduct a randomized field experiment on the website of Forbes Media, a major US media publisher. The ad-blocker users are divided into a treatment group, which receives the wall strategy, and a control group, which receives the acceptable ads strategy. We utilize the difference-in-differences method to estimate the causal effects. Our study shows that the wall strategy has an overall negative impact on user engagements. However, it has no statistically significant effect on high-engaged users as they would view the pages no matter what strategy is used. It has a big impact on low-engaged users, who have no loyalty to the site. Our study also shows that revisiting behavior decreases over time, but the ratio of session whitelisting increases over time as the remaining users have relatively high loyalty and high engagement. The paper concludes with discussions of managerial insights for publishers when determining counter-ad-blocking strategies.

The fast increase in ad blocker usage results in large revenue loss for online publishers and advertisers. Many publishers initialize counter-ad-blocking strategies, where a user has to choose either whitelisting the publisher's web site in their ad blocker or leaving the site without accessing the content. This paper aims to predict the user whitelisting behavior, which can help online publishers to better assess users' interests and design corresponding strategies. We present several techniques for personalized whitelist prediction for a target user and a target web page. Our prediction models are evaluated on real-world data provided by a large online publisher, Forbes Media. The best prediction performance was achieved using the gradient boosting regression tree model, which also demonstrated robustness and efficiency.

The revenue of online display advertising in the U.S. is projected to be 7.9 billion U.S. dollars by 2022. One main way of display advertising is through real-time bidding (RTB). In RTB, an ad exchange runs a second price auction among multiple advertisers to sell each ad impression. Publishers usually set up a reserve price, the lowest price acceptable for an ad impression. If there are bids higher than the reserve price, then the revenue is the higher price between the reserve price and the second highest bid; otherwise, the revenue is zero. Thus, a higher reserve price can potentially increase the revenue, but with higher risks associated. In this paper, we study the problem of estimating the failure rate of a reserve price, i.e., the probability that a reserve price fails to be outbid. The solution to this problem have managerial implications to publishers to set appropriate reserve prices in order to minimizes the risks and optimize the expected revenue. This problem is highly challenging since most publishers do not know the historical highest bidding prices offered by RTB advertisers. To address this problem, we develop a parametric survival model for reserve price failure rate prediction. The model is further improved by considering user and page interactions, and header bidding information. The experimental results demonstrate the effectiveness of the proposed approach.

Display advertising is the most important revenue source for publishers in the online publishing industry. The ad pricing standards are shifting to a new model in which ads are paid only if they are viewed. Consequently, an important problem for publishers is to predict the probability that an ad at a given page depth will be shown on a user's screen for a certain dwell time. This paper proposes deep learning models based on Long Short-Term Memory (LSTM) to predict the viewability of any page depth for any given dwell time. The main novelty of our best model consists in the combination of bi-directional LSTM networks, encoder-decoder structure, and residual connections. The experimental results over a dataset collected from a large online publisher demonstrate that the proposed LSTM-based sequential neural networks outperform the comparison methods in terms of prediction performance.

A half of online display ads are not rendered viewable because the users do not scroll deep enough or spend sufficient time at the page depth where the ads are placed. In order to increase the marketing efficiency and ad effectiveness, there is a strong demand for viewability prediction from both advertisers and publishers. This paper aims to predict the dwell time for a given triplet based on historic data collected by publishers. This problem is difficult because of user behavior variability and data sparsity. To solve it, we propose predictive models based on Factorization Machines and Field‐aware Factorization Machines in order to overcome the data sparsity issue and provide flexibility to add auxiliary information such as the visible area of a user's browser. In addition, we leverage the prior dwell time behavior of the user within the current page view, that is, time series information, to further improve the proposed models. Experimental results using data from a large web publisher demonstrate that the proposed models outperform comparison models. Also, the results show that adding time series information further improves the performance.

Online display advertising has becomes a billion-dollar industry, and it keeps growing. Advertisers attempt to send marketing messages to attract potential customers via graphic banner ads on publishers' webpages. Advertisers are charged for each view of a page that delivers their display ads. However, recent studies have discovered that more than half of the ads are never shown on users' screens due to insufficient scrolling. Thus, advertisers waste a great amount of money on these ads that do not bring any return on investment. Given this situation, the Interactive Advertising Bureau calls for a shift toward charging by viewable impression, i.e., charge for ads that are viewed by users. With this new pricing model, it is helpful to predict the viewability of an ad. This paper proposes two probabilistic latent class models (PLC) that predict the viewability of any given scroll depth for a user-page pair. Using a real-life dataset from a large publisher, the experiments demonstrate that our models outperform comparison systems.

The amount of time spent by users at specific page depths within webpages, called dwell time, can be used by web publishers to decide where to place online ads and what type of ads to place at different depths within a webpage. This paper presents a model to predict the dwell time for a given "user, webpage, depth" triplet based on historic data collected by publishers. Dwell time prediction is difficult due to user behavior variability and data sparsity. We adopt the Factorization Machines model because it is able to capture the interaction between users and webpages, overcome the data sparsity issue, and provide flexibility to add auxiliary information such as the visible area of a user's browser. Experimental results using data from a large web publisher demonstrate that our model outperforms deterministic and regression-based comparison models.

Mobile participatory sensing could be used in many applications such as vehicular traffic monitoring, pollution tracking, or even health surveying. However, its success depends on finding a solution for querying large numbers of users which protects user location privacy and works in real-time. This paper presents PAMPAS, a privacy-aware mobile distributed system for efficient data aggregation in mobile participatory sensing. In PAMPAS, mobile devices enhanced with secure hardware, called secure probes (SPs), perform distributed query processing, while preventing users from accessing other users' data. A supporting server infrastructure (SSI) coordinates the inter-SP communication and the computation tasks executed on SPs. PAMPAS ensures that SSI cannot link the location reported by SPs to the user identities even if SSI has additional background information. In addition to its novel system architecture, PAMPAS also proposes two new protocols for privacy-aware location-based aggregation and adaptive spatial partitioning of SPs that work efficiently on resource-constrained SPs. Our experimental results and security analysis demonstrate that these protocols are able to collect the data, aggregate them, and share statistics or derived models in real-time, without any location privacy leakage.

As a massive industry, display advertising delivers advertisers' marketing messages to attract customers through graphic banners on webpages. Advertisers are charged by ad serving, where their ads are shown in web pages. However, recent studies show that about half of the ads were actually never seen by users because they do not scroll deep enough to bring the ads in-view. Thus, the ad pricing standards are shifting to a new model: ads are paid if they are in view, not just being served. To the best of our knowledge, this paper is the first to address the important problem of ad viewability prediction which can improve the performance of guaranteed ad delivery, real-time bidding, as well as recommender systems. We analyze a real-life dataset from a large publisher, identify a number of features that impact the scroll depth for a given user and a page, and propose a probabilistic latent class model that predicts the viewability of any given scroll depth for a user-page pair. The experiments demonstrate that our model outperforms comparison systems based on singular value decomposition and logistic regression, in terms of prediction quality and training time.