Web Publishers Research Articles

The Critique toward Uṣūl al-Fiqh Literacy of 1890–2023: An Offer for Development

Critique of uṣūl al-fiqh literacy is very crucial. The scarcity of critique can lead to the risk of intellectual stagnation and an inability to address the legal needs of a constantly changing society, simultaneously causing a lack of resonance with reconstructive ideas. The study aims: 1) to explore evidence of the scarcity of studies criticizing uṣūl al-fiqh from 1890 to 2023; 2) to explain the factors influencing the scarcity of critical works, and 3) to propose steps for the reconstruction of uṣūl al-fiqh. The data sources for this article are works collected from web publishers, digital libraries, and search engines. The data obtained was mapped and analyzed, and its trends were recorded. This study finds that during the period 1890-2023, criticism was the rarest aspect in uṣūl al-fiqh literature. The factors causing the scarcity of critique vary and complement each other, involving scholarly elements, uṣūl al-fiqh factors, and surrounding policies. Although criticism of uṣūl al-fiqh is relatively rare, it does not mean there is no space for reconstruction. Through this study, readers can understand the recent developments in uṣūl al-fiqh and assess the extent to which criticism has been undertaken while reflecting on how reconstruction can be initiated once again.

Impact and development of an Open Web Index for open web search

AbstractWeb search is a crucial technology for the digital economy. Dominated by a few gatekeepers focused on commercial success, however, web publishers have to optimize their content for these gatekeepers, resulting in a closed ecosystem of search engines as well as the risk of publishers sacrificing quality. To encourage an open search ecosystem and offer users genuine choice among alternative search engines, we propose the development of an Open Web Index (OWI). We outline six core principles for developing and maintaining an open index, based on open data principles, legal compliance, and collaborative technology development. The combination of an open index with what we call declarative search engines will facilitate the development of vertical search engines and innovative web data products (including, e.g., large language models), enabling a fair and open information space. This framework underpins the EU‐funded project OpenWebSearch.EU, marking the first step towards realizing an Open Web Index.

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About The Author

Setting Reserve Prices in Second-Price Auctions with Unobserved Bids

In this work we consider a seller who sells an item via second-price auctions with a reserve price. By controlling the reserve price, the seller can influence the revenue from the auction, and in this paper, we propose a method for learning optimal reserve prices. We study a limited information setting where the probability distribution of the bids from bidders is unknown and the values of the bids are not revealed to the seller. Furthermore, we do not assume that the seller has access to a historical data set with bids. Our main contribution is a method that incorporates knowledge about the rules of second-price auctions into a multiarmed bandit framework for optimizing reserve prices in our limited information setting. The proposed method can be applied in both stationary and nonstationary environments. Experiments show that the proposed method outperforms state-of-the-art bandit algorithms. In stationary environments, our method outperforms these algorithms when the horizon is short and performs as good as they do for longer horizons. Our method is especially useful if there is a high number of potential reserve prices. In addition, our method adapts quickly to changing environments and outperforms state-of-the-art bandit algorithms designed for nonstationary environments. Summary of Contribution: A key challenge in online advertising is the pricing of advertisements in online auctions. The scope of our study is second-price auctions with a focus on the reserve price optimization problem from a seller’s point of view. This problem is motivated by the real-life practice of small and medium-sized web publishers. However, the proposed solution approach is applicable to any seller who sells an item via second-price auctions and wants to optimize its reserve price during these auctions. Our solution approach is based on techniques from machine learning and operations research, and it would be beneficial especially for sellers who start the selling process without any historical data and can collect the data on the outcomes of the auctions while making reserve price decisions over time. History: Accepted by RamRamesh, Area Editor for Data Science & Machine Learning. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/ijoc.2022.1199 .

Maximizing revenue for publishers using header bidding and ad exchange auctions

We study how web publishers should set their floor prices in order to maximize expected revenues when they have access to two selling mechanisms, namely an ad exchange and header bidding, in order to sell impressions on the real-time bidding market. We consider the publisher’s problem under incomplete information, propose bandit-type algorithms, and show that their regret – the performance loss compared to the optimal algorithm – is sub-linear in the time horizon. Experiments illustrate the effectiveness of our algorithms.

Brands as a nation: An analysis of overseas media engagement of top Chinese brands

Exposure and engagement to the media coverage of Chinese brands among average consumers outside of the domestic Chinese market can serve as an important measure of China’s global influence. In this study, we analyzed the audience engagement of online articles about the top 20 Chinese brands from over 5000 web publishers around the world in a period of 5 months from October 2017 to February 2018. Specific results from our analyses point to a lack of sustained effort, careful coordination, and proper control in these companies’ public relations and crisis communication. The study also reveals a larger problem in China’s public diplomacy efforts. The top Chinese firms, despite their high market value, are yet to establish soft influence internationally. The global media engagements of Chinese companies are largely driven, passively, by special events and crises. Moreover, our study offers a new way of studying China and global media by leveraging large sets of industry data and adopting a set of audience matrices commonly used by media publishers to examine social science research questions.

Research on CTR prediction based on stacked autoencoder

Click-through rate prediction is critical in internet advertising and affects web publisher’s profits and advertiser’s payment. In the CTR prediction, mining the interaction between features and extracting user interest are key factors affecting the prediction rate. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features and user interest in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, and uses the bidirectional gated recurrent unit (Bi-GRU) to extract user interest. We utilize stacked autoencoder to portray the nonlinear associated relationship of data. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in internet advertising.

Research on CTR Prediction Based on Deep Learning

Click-through rate (CTR) prediction is critical in Internet advertising and affects web publisher’s profits and advertiser’s payment. In the CTR prediction, the interaction between features is a key factor affecting the prediction rate. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition and combines the power of field-aware factorization machines and deep learning to portray the nonlinear associated relationship of data to solve the sparse feature learning problem. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.

A New Approach for Advertising CTR Prediction Based on Deep Neural Network via Attention Mechanism.

Click-through rate prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, takes advantage of the attention mechanism in neural network modelling, and improves FM to make feature interactions contribute differently to the prediction. We utilize stack autoencoder to explore high-order feature interactions and use improved FM for low-order feature interactions to portray the nonlinear associated relationship of features. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.

A hierarchical framework for ad inventory allocation in programmatic advertising markets

Abstract Enabled by the big data driven user profiling and precision bidding techniques, online programmatic advertising markets have evolved from the traditional website-buying or ad-slot-buying models to a fine-grained and real-time trading model at the level of ad impressions (i.e., ad inventory). As a result, Web publishers are now facing a challenging decision of allocating the ad inventory across multiple advertising models, which has a direct and important influence on both their individual revenues, and the market-wide supply-demand balance. In this paper, we propose a novel hierarchical ad inventory allocation framework (AIAF), taking into consideration the possible scenarios of ad inventory allocation in programmatic advertising markets. AIAF explicitly captures the specific features of ad inventory allocation in each of three levels (i.e., channel level, market level and platform level), and also their influence-feedback effects. We present the general solution process for solving this model on the basis of its property analysis. An illustrative instantiation of our AIAF model is formulated to demonstrate its applications in supporting publishers’ decision-making on the ad inventory allocation. We also conduct experiments based on empirical data so as to validate the model and analysis. Our research findings indicate that 1) our AIAF model outperforms other single-level and two-level allocation strategies; 2) the fine-grained optimization is superior to that of the coarse-grained level; 3) allocation decisions should be made on the basis of the comparative marginal revenue instead of the absolute marginal revenue.

A defense of ad blocking and consumer inattention

Ad blockers are a category of computer software program, typically run as web browser extensions, that allow users to selectively eliminate advertisements from the webpages they visit. Many people have alleged that using an ad blocker is morally problematic because it is bad for content providers and consumers, and it is morally akin to theft. We disagree. In this paper, we defend an independent argument for the conclusion that using an ad blocker is morally permissible. In doing so, we respond to the criticisms that ad blocking is bad for content providers and consumers, that it is morally akin to theft, and that it violates a contract between consumers and web publishers.

Cost-per-Impression Pricing for Display Advertising

Display advertising has a 39% share of the online advertising market and is its fastest-growing category. In this paper, we consider an online display advertising setting in which a web publisher posts display ads on its website and charges based on the cost-per-impression (CPM) pricing scheme while promising to deliver a certain number of impressions on the ads posted. The publisher faces uncertain demand for advertising slots and uncertain supply of visits from viewers. We formulate the problem as a queueing system, where the advertising slots correspond to service channels with the service rate of each server synchronized with other active servers. We determine the publisher’s optimal price to charge per impression and show that it can increase in the number of impressions made of each ad, which is in contrast to the quantity discount commonly offered in practice. We show that the optimal CPM price may increase in the number of ads rotating among slots. This result is typically not expected because an increase in the number of rotating ads in the system can be interpreted as an increase in the service capacity. However, the capacity increase leads to an increase in the fill rate of the demand (the portion of demand satisfied by the publisher). Hence, the publisher can afford to optimally decrease the arrival rate by increasing the price. The electronic companion is available at https://doi.org/10.1287/opre.2017.1697 .

Predictive models and analysis for webpage depth‐level dwell time

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.

To use or not to use ad blockers? The roles of knowledge of ad blockers and attitude toward online advertising

The rapid spread of ad blockers potentially threatens the sustainability of the hitherto dominant business model of ad-supported websites, in which users get web content free in return for allowing themselves to be exposed to advertising. Focusing on the users’ perspective, this study proposes that (a) adoption of ad blockers is positively influenced by the level of knowledge of their advantageous features; (b) the decision to continue using ad blockers is negatively affected by attitude toward online advertising; and (c) this attitude is positively shaped by perceptions of online advertising’s pleasure, credibility, and economic benefits, as well as negatively shaped by perceptions of online advertising’s intrusiveness and clutter. We tested these relationships in a survey study among the members of an online panel supported by the Spanish advertising industry, and all the relationships were confirmed within a structural equation model. Our findings provide some implications for online advertising stakeholders. Web publishers and online advertisers may expect that, in the coming years, ad blockers will continue to spread rapidly as a consequence of the extension of their knowledge among Internet users. These stakeholders are thus advised to focus on improving Internet users’ experiences with online advertising by reducing those ad characteristics that provoke negative reactions and by strengthening those that are positively evaluated.

Survey on Revenue Management in Media and Broadcasting

Advertisements are a key source of revenue for companies in the broadcasting and web industries. However, because of increasing competition, advertisers and web publishers have been forced to find innovative ways to increase their profits and gain competitive advantages. Revenue management is a useful operations research and management science tool that may be used to do so. In this paper, we provide an updated review of revenue-management research conducted in the broadcasting and online advertisement industries, highlighting the strategies and techniques adopted to maximize advertising revenue. We also identify mobile advertising as an emerging revenue-management application and review current research on it. We conclude by identifying potential gaps that future research might address.

Optimal advertising on a two-dimensional web banner

The rapid growth and proliferation of internet based services has opened up new avenues of interactions and corresponding business models. Advertisements shown within websites form a major source of revenue for web publishers. Naturally, the proper planning and allocation of advertisement space in the webpages becomes a major factor in the profitability of the business model. This paper addresses the problem of determining efficient placement of advertisements on a two-dimensional web banner with the objective of maximizing revenue. An integer programming mathematical model is developed here to optimally place advertisements on a two-dimensional banner. The performance of the new formulation is compared with models existing in the recent literature. The proposed model is also validated with computational analysis to prove its efficacy.

Optimal Placement of Advertisements on a Pixelated Web Banner

The widespread usage of web applications has made it a popular medium of advertising, at the same time creating competition for advertising space. Web publishers seek to get maximum revenues utilising the advertisement space optimally. In this paper, a mathematical model is proposed to schedule the competing advertisements on multiple web banners with varying cost bids per pixel with a revenue maximization objective. A case study has been discussed to illustrate the application of the model and is solved using optimization software LINGO.

Policy Approval Engine - A Framework for Securing Web Applications and Web User

Background/Objectives: Web applications face variety of new extortions related to injections. Securing the web applications becomes paramount and an intricate process with the current technologies. The objective of this paper is to protect the web application form injection attacks. Methods/Statistical Analysis: Web publishers frequently integrate third-party advertisements into web pages that also contain sensitive end-user personal data. This may expose sensitive page content to confidentiality and integrity attacks launched by advertisements. Thus web browser needs some simple security policy and enforcement which can alleviate basic attacks in order to guard the applications and user that resides on the web. Findings: The policy enforcement framework for addressing security threats and to protect against cross-site request forgery, cross-site scripting, and content stealing has been proposed. To do so, the framework observes all outgoing web requests within the browser and offers authorization and approval checks before the contents are embedded into a page. Additionally, the advertisements are restricted to the access the user data. Thus, the paper delivers better understanding about web application security policy enforcement which protects user data from interactive ads. The proposed framework is compared with existing methods like SOMA and RequestPolicy and the result shows that the proposed method improves better security against attacks. The proposed framework decreases the false positive rate and false negative rate when compared to the existing framework. The accuracy of the proposed method is above 90%. Applications/Improvements: The proposed framework can be used to protect the web against cross-site request forgery, cross-site scripting, and content stealing. The future work focuses on providing security against web site defacement and other attacks.

Display Advertising Auctions with Arbitrage

Online display advertising exchanges connect Web publishers with advertisers seeking to place ads. In many cases, the advertiser obtains value from an ad impression (a viewing by a user) only if it is clicked, and frequently advertisers prefer to pay contingent on this occurring. But at the same time, many publishers demand payment independent of clicks. Arbitragers with good estimates of click-probabilities can resolve this conflict by absorbing the risk and acting as an intermediary, paying the publisher on allocation and being paid only if a click occurs. This article examines the incentives of advertisers and arbitragers and contributes an efficient mechanism with truthful bidding by the advertisers and truthful reporting of click predictions by arbitragers as dominant strategies while, given that a hazard rate condition is satisfied, yielding increased revenue to the publisher. We provide empirical evidence based on bid data from Yahoo's Right Media Exchange suggesting that the mechanism would increase revenue in practice.

Traffic Shaping to Optimize Ad Delivery

Web publishers must balance two objectives: how to keep users engaged by directing them to relevant content, and how to properly monetize this user traffic. The standard approach is to solve each problem in isolation, for example, by displaying content that is tailored to the user’s interests so as to maximize clickthrough rates (CTR), and also by building a standalone ad serving system that displays ads depending on the user’s characteristics, the article being viewed by the user, and advertiser-specified constraints. However, showing the user only those articles with highest expected CTR precludes the display of some ads; if the publisher had previously guaranteed the delivery of a certain volume of impressions to such ads, then underdelivery penalties might have to be paid. We propose a joint optimization of article selection and ad serving that minimizes underdelivery by shaping some of the incoming traffic to pages where underperforming ads can be displayed, while incurring only minor drops in CTR. In addition to formulating the problem, we design an online optimization algorithm that can find the optimal traffic shaping probabilities for each new user using only a cache of one number per ad contract. Experiments on a large real-world ad-serving Web portal demonstrate significant advantages over the standalone approach: a threefold reduction in underdelivery with only 10% drop in CTR, or a 2.6-fold reduction with a 4% CTR drop, and similar results over a wide range.