Some of the world’s most well-known IT companies are in fact advertising companies deriving their primary revenues through digital advertising. For this reason, these IT giants are able to continually drive the evolutions of information technology in ways that serve to enhance our everyday lives. The benefits of this relationship include free web browsers with powerful search engines and mobile applications. Still, it turns out that “free” comes at a cost that is paid through our interactions within a digital advertising ecosystem. Digital advertising is not without its challenges. Issues originate from the complex platforms utilized to support advertising over web and mobile application interfaces. This is especially true for advertising links. Additionally, as new methods for advertising develop so too does the potential for impacting its underlying ecosystem for good or ill. Accordingly, researchers are interested in understanding this ecosystem, the factors that impact it, and the strategies for improving it. The major contribution of this survey is that it is the first review of the digital advertising ecosystem as it applies to online websites and mobile applications. In doing so, we explain the digital advertising relationships within this ecosystem along with their technical, social, political, and physical implications. Furthermore, advertising principles along with a variation of other advertising approaches, both legitimate and malicious, are explored in order to compare and contrast competing digital advertising methods.

In-Depth Survey of Digital Advertising Technologies

Analizira se korpus reklamnih tekstova s hrvatskih internetskih stranica. Reklame se grupiraju tematski s obzirom na virtualni prostor, identitet i prikazivanje tjelesnosti. Naglasene su opce osobine reklamnoga diskursa te su izdvojene pojedine specificnosti reklamiranja na Internetu.

Advertising on the Internet

Truss was proposed to study social network data represented by graphs. A k-truss of a graph is a cohesive subgraph, in which each edge is contained in at least k-2 triangles within the subgraph. While truss has been demonstrated as superior to model the close relationship in social networks and efficient algorithms for finding trusses have been extensively studied, very little attention has been paid to truss maintenance. However, most social networks are evolving networks. It may be infeasible to recompute trusses from scratch from time to time in order to find the up-to-date $k$-trusses in the evolving networks. In this paper, we discuss how to maintain trusses in a graph with dynamic updates. We first discuss a set of properties on maintaining trusses, then propose algorithms on maintaining trusses on edge deletions and insertions, finally, we discuss truss index maintenance. We test the proposed techniques on real datasets. The experiment results show the promise of our work.

Efficient Truss Maintenance in Evolving Networks.

Einführung in die Emotionspsychologie.

With the increasing share of online advertising, the amount of highly detailed data available on an individual consumer level has substantially increased over the last few years. Because there are costs associated with collecting, analyzing, and storing this type of data, it is important to understand the value of this specific type of personal data. In this paper, we illustrate the economic value of clickstream data by highlighting the direct and interaction effects of multiple types of online advertising on an individual user-level. By developing a binary logit model with Bayesian estimation techniques to predict consumer behavior and by analyzing clickstream data from a large financial service provider, we find strong differences in the effects of repeated ad exposures. Along with uncovering important insights, such as the positive cross-channel effects of display and paid search advertising, which are important when managing online advertising campaigns, we demonstrate how the value of clickstream data can be estimated to support decision-making in the emerging context of real-time bidding (RTB). keywords: personal data valuation, clickstream analysis, online advertising efficiency, consumer behavior, Bayesian estimation Proceedings of the 21st European Conference on Information Systems

The Economic Value of Clickstream Data From an Advertiser's Perspective

AdMotional is aiming at achieving a win-win situation for online advertisers and web users alike through optimization of the campaign selection process and the creation of individually personalized ads. This approach is expected to result in increased campaign performance for advertisers, and in more relevant – and thus less annoying – ads for consumers. We present a general overview of the system architecture before describing the main components in detail. For campaign selection, AdMotional combines and enhances state-of-the-art targeting technologies by a novel concept of emotional targeting. The key aspect of ad personalization furthermore demands for the dynamic creation of the ad itself for which different approaches are presented and evaluated. We then introduce the learning and optimization component and strategies before concluding with a summary and brief outlook into future developments.

http://www.admotional.org/wp-content/uploads/presse/wims/AdMotional_BreakingNews_WIMS11.pdf

ADMOTIONAL: Towards personalized online ads

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ADMOTIONAL: Towards personalized online ads