Digital Advertising: An Information Scientist's Perspective

  title={Digital Advertising: An Information Scientist's Perspective},
  author={James G. Shanahan and Goutham Kurra},
  booktitle={Advanced Topics in Information Retrieval},
Digital online advertising is a form of promotion that uses the Internet and Web for the express purpose of delivering marketing messages to attract customers. Examples of online advertising include text ads that appear on search engine results pages, banner ads, in-text ads, or Rich Media ads that appear on regular web pages, portals, or applications. Over the past 15 years online advertising, a $65 billion industry worldwide in 2009, has been pivotal to the success of the Web. That being said… 
Competition between demand-side intermediaries in ad exchanges
Online advertising constitutes one of the main sources of revenue for the majority of businesses on the web. Online advertising inventory was traditionally traded via bilateral contracts between
Click-through Prediction for Advertising in Twitter Timeline
A learning-to-rank method is proposed which not only addresses the sparsity of training signals but also can be trained and updated online and its superiority over the current production model adopted by Twitter is demonstrated.
Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising
Confident that Advertising with Artificial Intelligence and Machine Learning are here for a noticeable and a significant change in the process of Advertising.
Admotional: Towards Personalized Online Ads
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Application Configuration Repository for Adaptive Service-Based Systems: Overcoming Challenges in an Evolutionary Online Advertising Environment
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Contextual Search: A Computational Framework
  • M. Melucci
  • Computer Science
    Found. Trends Inf. Retr.
  • 2012
This paper presents a meta-search architecture that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and evaluating documents for relevance and quality.


Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
The goal of this workshop is to not only increase communication between researchers working on seemingly different pieces of the advertisement pie, but to encourage data mining researchers to bring new ideas from related areas to solve the numerous challenges faced by the rapidly changing digital advertising industry.
A search-based method for forecasting ad impression in contextual advertising
Experimental results show that the approach can accurately forecast the expected number of impressions of contextual ads in real time, and how this method can be used in tools for bid selection and ad evaluation.
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This work is the first empirical study for BT on the click-through log of real world ads and draws three important conclusions: users who clicked the same ad will truly have similar behaviors on the Web, Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search.
A semantic approach to contextual advertising
A system for contextual ad matching based on a combination of semantic and syntactic features is proposed, which will help improve the user experience and reduce the number of irrelevant ads.
Impedance coupling in content-targeted advertising
This work proposes ten strategies for solving the problem of associating ads with a Web page from a computer science perspective and suggests that great accuracy in content-targeted advertising can be attained with appropriate algorithms.
Automatic generation of bid phrases for online advertising
Empirical evaluation based on a real-life corpus of advertiser-created landing pages and associated bid phrases confirms the value of the approach, which successfully re-generates many of the human-crafted bid phrases and performs significantly better than a pure text extraction method.
Just-in-time contextual advertising
Empirical evaluation proves that matching ads on the basis of a carefully selected 5% fraction of the page text sacrifices only 1%-3% in ad relevance, and is competitive with matching based on the entire page content.
Predicting clicks: estimating the click-through rate for new ads
This work shows that it can be used to use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads, and shows that using this model improves the convergence and performance of an advertising system.
Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks
A new data set that represents the adoption of a new telecommunications service is used, and very strong support is shown for the hypothesis that network linkage can directly affect product/service adoption is shown.
Since the late 1990s, online shopping has taken off as an increasing number of consumers purchase increasingly diversified products on the Internet. Given that how to attract and retain consumers is