Behavioral Targeting Online Advertising

With the rapid growth of the online advertising market, Behavioral Targeting (BT), which delivers advertisements to users based on understanding of their needs through their behaviors, is attracting more attention. The amount of spend on behaviorally targeted ad spending in the US is projected to reach $4.4 billion in 2012 (Hallerman, 2008). BT is a complex technology, which involves data collection, data mining, audience segmentation, contextual page analysis, predictive modeling and so on. This chapter gives an overview of Behavioral Targeting by introducing the Behavioral Targeting business, followed by classic BT research challenges and solution proposals. We will also point out BT research challenges which are currently under-explored in both industry and academia.

Adapting Online Advertising Techniques to Television

The availability of precise data on TV ad consumption fundamentally changes this advertising medium, and allows many techniques developed for analyzing online ads to be adapted for TV. This chapter looks in particular at how results from the emerging field of online ad quality analysis can now be applied to TV.

An Explorative Study of Virtual Product Placement

With the aid of content-based multimedia analysis, virtual product placement opens up new opportunities for advertisers to effectively monetize the existing videos in an efficient way. In addition, a number of significant and challenging issues are raising accordingly, such as how to less-intrusively insert the contextually relevant advertising message (what) at the right place (where) and the right time (when) with the attractive representation (how) in the videos. In this chapter, domain knowledge in support of delivering and receiving the advertising message is introduced, such as the advertising theory, psychology and computational aesthetics. We briefly review the state of the art techniques for assisting virtual product placement in videos. In addition, we present a framework to serve the virtual spotlighted advertising (ViSA) for virtual product placement and give an explorative study of it. Moreover, observations about the new trend and possible extension in the design space of virtual product placement will also be stated and discussed. We believe that it would inspire the researchers to develop more interesting and applicable multimedia advertising systems for virtual product placement.

Audience Intelligence in Online Advertising

This chapter introduces the fundamentals of audience intelligence’s important aspects. The goal is to present what are related to audience intelligence, how online audience intelligence could be done, and some representative methods. In this chapter, the author will first address the fundamentals of the audience intelligence, including the brief introduction of the online ad eco-system, the relationship between audience intelligence and existing online ad types, performance measures and the challenges in this field. Next, some classical methods of audience intelligence on end-users will be introduces, namely, demographic, geographic, behavioral targeting and online commercial intent (OCI) detection. Then, audience intelligence on advertisers will be presented. Finally, related topics of online advertising, such as the privacy issue, will be addressed.

Distributed Technologies for Personalized Advertisement Delivery

This chapter provides an overview on personalized advertisement delivery paradigms on the web with a focus on the recommendation of advertisements expressed in or accompanied by text. Different methods of online targeted advertising will be examined, while justifying the need for channeling the appropriate ads to the corresponding users. The aim of the work presented here is to illustrate how the semantic representation of ads and user preferences can achieve optimal and unobtrusive ad delivery. We propose a set of distributed technologies that efficiently handles the lack of textual data in ads by enriching ontological knowledge with statistical contextual data in order to classify ads and generic content under a uniform, machine-understandable vocabulary. This classification is used to construct lightweight semantic user profiles, matched with semantic ad descriptions via fuzzy semantic reasoning. A real world user study, as well as an evaluative exploration of framework alternatives validate the system’s effectiveness to produce high quality ad recommendations.

The Advent of Play and Pursuit

Multimedia advertising on the internet has demanded that advertisers and marketers become more creative and adventurous in their pursuit of consumers. This chapter argues that the notions of play and pursuit are intrinsic components of persuading consumers to interact and engage with advertising. Online advertising is also simulating game environments to reach consumers through an alternate reality. These techniques elevate play as a persuasive tool to entice consumers and to capture data for advertisers. It tacitly thwarts the assumption that the internet is a space of consumer empowerment and control. Instead it reinforces the hand of capital and uses the architecture and features of the internet to make virtual environments a productive space for advertisers.

Utilizing Sentiments in Online Contextual Advertising

Ever increasing number of internet users has attracted many of the companies on the internet for promoting their product and services. This has led to the development of new age of advertising called online or web advertising. The objective of this chapter is two-fold. First, it introduces concepts involved in online advertising. Second, it proposes a novel conceptual framework for contextual online advertising which attempts to utilize local context and sentiment for identifying relevant ads. Contextual advertising is an important class of online advertising in which ads are displayed automatically on web pages based on their content. The proposed framework works in two stages. The first stage retrieves ads for placement. The second stage uses sentiment analysis to filter out ads that do not agree with the sentiments (positive or negative polarity) being expressed in the document. The polarity is identified using SentiWordNet and context-based heuristics.

Contextual In-Stream Video Advertising

With Internet delivery of video content surging to an unprecedented level, online video advertising is becoming increasingly pervasive. In this chapter, we present a new advertising paradigm for online video, called contextual in-stream video advertising, which automatically associates the most relevant video ads with online videos and seamlessly inserts the ads at the most appropriate spatiotemporal positions within each individual video. Different from most current video-oriented sites that only display the ads at the predefined locations in a video, this advertising paradigm aims to embed more contextually relevant ads at less intrusive positions within the video stream nonlinearly. We introduce the following key techniques in this paradigm: video processing for ad location detection, text analysis for ad selection, and optimization for ad insertion. We also describe two recently developed systems as showcases, i.e., VideoSense and AdOn which support in-stream inline and overlay advertising, respectively.

Click-Through Rate Estimation for Rare Events in Online Advertising

In online advertising campaigns, to measure purchase propensity, click-through rate (CTR), defined as a ratio of number of clicks to number of impressions, is one of the most informative metrics used in business activities such as performance evaluation and budget planning. No matter what channel an ad goes through (display ads, sponsored search or contextual advertising), CTR estimation for rare events is essential but challenging, often incurring with huge variance, due to the sparsity in data. In this chapter, to alleviate this sparsity, we develop models and methods to smoothen CTR estimation by taking advantage of the natural data hierarchy or by clustering and data continuity in time to leverage information from data close to the events of interest. In a contextual advertising system running at Yahoo!, we demonstrate that our methods lead to significantly more accurate estimation of CTRs.

Targeted Mobile Advertisement in the IP Multimedia Subsystem

This chapter provides an overview on targeted advertisement in the IP Multimedia Subsystem (IMS). A new entity called Personalization and Advertisement Insertion Logic (PAIL) is introduced, which enables a mobile network operator to exploit contextual data stored in its network for personalized advertisement selection. PAIL combines location information with user profile information in order to select the best match from a pool of advertisement clips. This selection is based on the Vector Space Model. For the evaluation of this framework a series of tests with users were executed. These tests show that using contextual information from the IMS network a subjective better match of advertisement clips with user interests is achievable.