Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

XRay: Enhancing the Web's Transparency with Differential Correlation

Today's Web services - such as Google, Amazon, and Facebook - leverage user data for varied purposes, including personalizing recommendations, targeting advertisements, and adjusting prices. At present, users have little insight into how their data is being used. Hence, they cannot make informed choices about the services they choose. To increase transparency, we developed XRay, the first fine-grained, robust, and scalable personal data tracking system for the Web. XRay predicts which data in an arbitrary Web account (such as emails, searches, or viewed products) is being used to target which outputs (such as ads, recommended products, or prices). XRay's core functions are service agnostic and easy to instantiate for new services, and they can track data within and across services. To make predictions independent of the audited service, XRay relies on the following insight: by comparing outputs from different accounts with similar, but not identical, subsets of data, one can pinpoint targeting through correlation. We show both theoretically, and through experiments on Gmail, Amazon, and YouTube, that XRay achieves high precision and recall by correlating data from a surprisingly small number of extra accounts.Comment: Extended version of a paper presented at the 23rd USENIX Security Symposium (USENIX Security 14

Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines

Who's Feeding the Kids Online? Digital food marketing to children in Ireland: Advertisers’ tactics, children’s exposure and parents’ awareness

Obesity in children and young people is a global health challenge. The widespread marketing of unhealthy foods (food and non-alcoholic drinks high in fat, sugar and salt, or HFSS) plays a causal role in unhealthy eating and obesity. Food and eating is typically presented as an issue of ‘choice’. However, this disregards the fact that current obesogenic environments use many tactics to promote unhealthy foods, interfering with people’s ability to make good choices. This study examined: 1. Content appealing to children and young people on websites of top food and drink retail brands in Ireland 2. Marketing techniques on Facebook: Pages of food brands that have the highest reach among young teens, the first such study of which we are aware 3. Parents’ awareness of digital food marketing to their children in an online, two-stage survey with digital marketing examples and open-ended response options

Privacy-preserving targeted advertising scheme for IPTV using the cloud

In this paper, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of viewers/subscribers, a content provider (IPTV), an advertiser, and a cloud server. To provide high quality directed advertising service, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are published on the cloud server periodically (e.g. weekly) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the cloud, are considered (trade) secrets and therefore are protected as well. The cloud is oblivious to the published data, the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with a so-called {\em trapdoor} by the IPTV, can query the cloud and utilize the query results. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is suitable for practical usage

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

A survey of handover algorithms in DVB-H

Digital Video Broadcasting for Handhelds (DVB-H) is a standard for broadcasting IP Datacast (IPDC) services to mobile handheld terminals. Based on the DVB-T standard, DVB-H adds new features such as time slicing, MPE-FEC, in-depth interleavers, mandatory cell id identifier, optional 4K-modulation mode and the use of 5 MHz bandwidth in addition to the usually used 6, 7, or 8 MHz raster. IPDC over DVB-H is proposed for ETSI to complement the DVB-H standard by combining IPDC and DVB-H in an end-to-end system. Handover in such unidirectional broadcasting networks is a novel issue. In the last few years since the birth of DVB-H technology, great attention has been given to the performance analysis of DVB-H mobile terminals. Handover is one of the main research topics for DVB-H in mobile scenarios. Better reception quality and greater power efficiency are considered to be the main targets of handover research for DVB-H. New algorithms for different handover stages in DVB-H have been the subject of recent research and are currently being studied. Further novel algorithms need to be designed to improve the mobile reception quality. This article provides a comprehensive survey of the handover algorithms in DVB-H. A systematic evaluation and categorization approach is proposed based on the problems the algorithms solve and the handover stages being focused on. Criteria are proposed and analyzed to facilitate designing better handover algorithms for DVB-H that have been identified from the research conducted by the author