An overview of computational challenges in online advertising

Chatwin, Richard E.

doi:10.1109/acc.2013.6580778

Cited by 11 publications

(6 citation statements)

References 77 publications

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“…Based on the data in these profiles, publishers can show related advertisements to visitors that match their habits. In a location-based targeting, locationspecific advertisements are delivered to potential users; this technique is particularly useful for mobile advertising [36].…”

Section: Targeted Advertisingmentioning

confidence: 99%

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Online Advertising Security: Issues, Taxonomy, and Future Directions

Pooranian¹,

Conti²,

Haddadi³

et al. 2020

Preprint

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Online advertising has become the backbone of the Internet economy by revolutionizing business marketing. It provides a simple and efficient way for advertisers to display their advertisements to specific individual users, and over the last couple of years has contributed to an explosion in the income stream for several web-based businesses. For example, Google's income from advertising grew 51.6% between 2016 and 2018, to $136.8 billion. This exponential growth in advertising revenue has motivated fraudsters to exploit the weaknesses of the online advertising model to make money, and researchers to discover new security vulnerabilities in the model, to propose countermeasures and to forecast future trends in research.Motivated by these considerations, this paper presents a comprehensive review of the security threats to online advertising systems. We begin by introducing the motivation for online advertising system, explain how it differs from traditional advertising networks, introduce terminology, and define the current online advertising architecture. We then devise a comprehensive taxonomy of attacks on online advertising to raise awareness among researchers about the vulnerabilities of online advertising ecosystem. We discuss the limitations and effectiveness of the countermeasures that have been developed to secure entities in the advertising ecosystem against these attacks. To complete our work, we identify some open issues and outline some possible directions for future research towards improving security methods for online advertising systems.

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Section: Targeted Advertisingmentioning

confidence: 99%

“…The general models [32], [36] used by publishers to make money through advertising are determined based on the numbers of impressions, clicks, and actions. We explain each of these types of revenue model in detail in the following subsections.…”

Section: E Revenue Modelsmentioning

confidence: 99%

Online Advertising Security: Issues, Taxonomy, and Future Directions

Pooranian¹,

Conti²,

Haddadi³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, there have been substantial algorithmic advances that enhance targeting and optimization of advertising (Graepel et al 2010;Wang & Yuan 2015). Techniques such as multi-armed bandits define the search space and find the best matches given an overall target group or population (Chatwin 2013). Given that users can be targeted and analyzed individually, what purpose is there for aggregated data representations, such as personas?…”

Section: New Criticismmentioning

confidence: 99%

Are personas done? Evaluating their usefulness in the age of digital analytics

Salminen¹,

Jansen²,

An³

et al. 2018

Pers Stud

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In this critique, we conceptually examine the use of personas in an age of availability of large-scale online analytics data. Based on the criticism and benefits outlined in prior work, we formulate the major arguments for and against the use of personas, analyze these arguments, and demonstrate areas for the productive employment of personas by leveraging digital analytics data in their creation. From our review of the prior literature and the given availability of online customer data, our key tenet is that personas are located between aggregated and individual statistics. At their best, personas capture the coverage of the customer base attributed to aggregated data representations while retaining the interpretability of individual-level analytics. Persona creation benefits from both novel computational techniques and data sources. To demonstrate this, we propose and implement automatically generated personas primarily based on quantitative data. We also review key persona validation issues and examine how these issues can be addressed with automated persona generation using real user data from online analytics platforms. Finally, we outline areas of future research in the persona domain within the field of digital marketing and advertising. Overall, to survive in the rapidly developing marketing industry and online customer analytics, personas must evolve by adopting new practices. There are implications for this evolution of personas in a variety of domains, including design, content creation, and digital marketing.

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“…Sponsored search advertising campaign development involves organizing keywords into adgroups, developing adcopies for adgroups [8]. In the search advertising structure employed by major search engines (e.g., Google, Bing), under an advertiser's account, one or several campaigns run simultaneously to fulfill a certain promotional goal, where each campaign includes one or several adgroups, and each adgroup in turn contains one or more adcopies and a shared set of keywords.…”

Section: Introductionmentioning

confidence: 99%

Optimal Keywords Grouping in Sponsored Search Advertising Under Uncertain Environments

Yang

2020

International Journal of Electronic Commerce

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In sponsored search advertising, advertisers need to make a series of keyword decisions. Among them, how to group these keywords to form several adgroups within a campaign is a challenging task, due to the highly uncertain environment of search advertising. This paper proposes a stochastic programming model for keywords grouping, taking click-through rate and conversion rate as random variables, with consideration of budget constraints and advertisers' risktolerance. A branch-and-bound algorithm is developed to solve our model. Furthermore, we conduct computational experiments to evaluate the effectiveness of our model and solution, with two real-world datasets collected from reports and logs of search advertising campaigns. Experimental results illustrated that our keywords grouping approach outperforms five baselines, and it can approximately approach the optimum in a steady way. This research generates several interesting findings that illuminate critical managerial insights for advertisers in sponsored search advertising. First, keywords grouping does matter for advertisers, especially in the situation with a large number of keywords. Second, in keyword grouping decisions, the marginal profit does not necessarily show the marginal diminishing phenomenon as the budget increases. Such that, it's a worthy try for advertisers to increase their budget in keywords grouping decisions, in order to obtain additional profit. Third, the optimal keywords grouping solution is a result of multifaceted trade-off among various advertising factors. In particular, assigning more keywords into adgroups or having more budget won't certainly lead to higher profits. This suggests a warning for advertisers that it's not wise to take the number of keywords as the criterion for keywords grouping decisions.

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An overview of computational challenges in online advertising

Cited by 11 publications

References 77 publications

Online Advertising Security: Issues, Taxonomy, and Future Directions

Online Advertising Security: Issues, Taxonomy, and Future Directions

Are personas done? Evaluating their usefulness in the age of digital analytics

Optimal Keywords Grouping in Sponsored Search Advertising Under Uncertain Environments

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