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 advertising

IP Television (IPTV) has created a new arena for digital advertising that has not been explored to its full potential yet. IPTV allows users to retrieve on demand content and recommended content; however, very limited research has been applied in the domain of advertising in IPTV systems. The diversity of the field led to a lot of mature efforts in the fields of content recommendation and mobile advertising. The introduction of IPTV and smart devices led to the ability to gather more context information that was not subject of study before. This research attempts at studying the different contextual parameters, how to enrich the advertising context to tailor better ads for users, devising a recommendation engine that utilizes the new context, building a prototype to prove the viability of the system and evaluating it on different quality of service and quality of experience measures. To tackle this problem, a review of the state of the art in the field of context-aware advertising as well as the related field of context-aware multimedia have been studied. The intent was to come up with the most relevant contextual parameters that can possibly yield a higher percentage precision for recommending advertisements to users. Subsequently, a prototype application was also developed to validate the feasibility and viability of the approach. The prototype gathers contextual information related to the number of viewers, their age, genders, viewing angles as well as their emotions. The gathered context is then dispatched to a web service which generates advertisement recommendations and sends them back to the user. A scheduler was also implemented to identify the most suitable time to push advertisements to users based on their attention span. To achieve our contributions, a corpus of 421 ads was gathered and processed for streaming. The advertisements were displayed in reality during the holy month of Ramadan, 2016. A data gathering application was developed where sample users were presented with 10 random ads and asked to rate and evaluate the advertisements according to a predetermined criteria. The gathered data was used for training the recommendation engine and computing the latent context-item preferences. This also served to identify the performance of a system that randomly sends advertisements to users. The resulting performance is used as a benchmark to compare our results against. When it comes to the recommendation engine itself, several implementation options were considered that pertain to the methodology to create a vector representation of an advertisement as well as the metric to use to measure the similarity between two advertisement vectors. The goal is to find a representation of advertisements that circumvents the cold start problem and the best similarity measure to use with the different vectorization techniques. A set of experiments have been designed and executed to identify the right vectorization methodology and similarity measure to apply in this problem domain. To evaluate the overall performance of the system, several experiments were designed and executed that cover different quality aspects of the system such as quality of service, quality of experience and quality of context. All three aspects have been measured and our results show that our recommendation engine exhibits a significant improvement over other mechanisms of pushing ads to users that are employed in currently existing systems. The other mechanisms placed in comparison are the random ad generation and targeted ad generation. Targeted ads mechanism relies on demographic information of the viewer with disregard to his/her historical consumption. Our system showed a precision percentage of 69.70% which means that roughly 7 out of 10 recommended ads are actually liked and viewed to the end by the viewer. The practice of randomly generating ads yields a result of 41.11% precision which means that only 4 out of 10 recommended ads are actually liked by viewers. The targeted ads system resulted in 51.39% precision. Our results show that a significant improvement can be introduced when employing context within a recommendation engine. When introducing emotion context, our results show a significant improvement in case the user’s emotion is happiness; however, it showed a degradation of performance when the user’s emotion is sadness. When considering all emotions, the overall results did not show a significant improvement. It is worth noting though that ads recommended based on detected emotions using our systems proved to always be relevant to the user\u27s current mood

Video advertisement mining for predicting revenue using random forest

Shaken by the threat of financial crisis in 2008, industries began to work on the topic of predictive analytics to efficiently control inventory levels and minimize revenue risks. In this third-generation age of web-connected data, organizations emphasized the importance of data science and leveraged the data mining techniques for gaining a competitive edge. Consider the features of Web 3.0, where semantic-oriented interaction between humans and computers can offer a tailored service or product to meet consumers\u27 needs by means of learning their preferences. In this study, we concentrate on the area of marketing science to demonstrate the correlation between TV commercial advertisements and sales achievement. Through different data mining and machine-learning methods, this research will come up with one concrete and complete predictive framework to clarify the effects of word of mouth by using open data sources from YouTube. The uniqueness of this predictive model is that we adopt the sentiment analysis as one of our predictors. This research offers a preliminary study on unstructured marketing data for further business use

The Role of Self-congruity in Consumer Preferences: Perspectives from Transaction Records

Personalised marketing is more persuasive than traditional techniques aimed at the masses, however marketers do not always have access to consumers’ private attributes in order to apply these insights. The effect of personalisation is based on an established theory in consumer psychology – self-congruity theory – which posits that individuals prefer products, brands and advertisements that embody characteristics that match with their self-concepts. Self-congruence not only enhances marketing effectiveness, it can also be used to improve consumer well-being. While it has been established that consumers who spend in a way that is more congruent with their personality are happier, clarifications around the types of individuals who are more or less likely to engage in self-congruent spending, as well as the moderating effects on the benefit in happiness from such consumption could inform policy for improving happiness at a collective level. This thesis contributes to a growing body of research which attempts to understand how consumption patterns are related to consumers’ characteristics, its applications in advertising, as well as consumer well-being. By using a dataset containing more than 1 million transactions recorded over a period of 12-months, the thesis demonstrates the value of the digital footprint in the form of bank transactions for enriching our understanding of key questions in consumer research, underpinned by the theory of self-congruity. This thesis combines methods from computational social science with personality psychology to test research questions on consumer preferences. Two components of the thesis focused on the predictive utility of transaction records in inferring consumer attributes with which to personalise advertising, as well as the use of transaction records in examining self-congruence in overall consumption patterns and its relationship with happiness. Through five empirical studies, this work suggests that consumer attributes such as age and financial distress can be reliably inferred from consumption patterns reflected in transaction records (Chapter 3 and 5). The inferred age can be used to personalise advertisements in order to increase their appeal (Chapter 4). Using an objective measure of self-congruence in overall consumption pattern computed from transaction records and panel ratings, the thesis shows that individuals differ in their tendency to spend in a way that is congruent with their personality based on their levels of materialism and financial distress (Chapter 6). As the most important predictor of self-congruent spending, financial distress moderates the relationship between self-congruent spending and happiness (Chapter 7). These findings contribute insights into how consumption patterns are related to consumer attributes and usefulness for personalisation in marketing, as well as policy recommendations for improving well-being by targeting consumption patterns in financially distressed individuals. In addition, this thesis also showcases the value of machine learning and large-scale behavioural field data in the study of consumer psychology. Privacy and ethical concerns surrounding automated profiling and microtargeting are also cautioned

Rio Grande Hispanic consumers\u27 perceptions of Spanglish dialogue use in print and television advertising

Along with this population growth, greater affluence has occurred. Advertisers are taking notice of these facts and are scrambling to find the most effective way of marketing to Hispanics. Spanglish dialogue use in advertising is an increasingly popular method marketers use in attempts to advertise to Hispanics. The Texas/Mexico border provides a unique opportunity for the study of Spanglish in advertising. The population of the RGV identifies as overwhelmingly Hispanic, 89.1 percent according the U.S. Census Bureau (2006). The population of this region, then, represents the target audience of advertisements that utilize Spanglish dialogue. This study investigated the valence (positive, neutral or negative) Hispanics who live in the Rio Grande Valley hold toward advertisements that utilize Spanglish Dialogue. This study compared preference for Spanglish advertisements against English only advertisements. The study found that participants preferred English advertisements

Effects of location, size, and animation on the response rate to a promotional Web banner

The purpose of this study was to investigate the layout and design of promotional Web banners on a company home page. To accomplish this, three variables; location, size and animation were used to test their effect on the response rate to those banners. Response rates were calculated by dividing the total number of click-throughs to the banner by the total number of visits to the Web pages. In each case, a relationship existed between the variable and the response rate. The results of this study also show that both animation and size can have a positive effect on response rate. Furthermore, it was found that location was not as effective as the other variables in the determination of response rates to the promotional banners. Marketing managers should consider these and other variables when designing and constructing company Web sites

Embedded Advertising and the Venture Consumer

Embedded advertising—marketing that promotes brands from within entertainment content—is a thriving, rapidly changing practice. Analysts estimate that embedded advertising expenditures will exceed $10 billion in 2010. The market continues to grow even as traditional advertising revenues contract. The relatively few legal scholars who have studied embedded advertising believe that it is under-regulated. Ineffective regulation, they claim, is deeply troubling because corporations may, with legal impunity, deceptively pitch products to trusting viewers. Critics charge that embedded advertising creates hyper-commercialism, distorts consumers\u27 tastes, taints the artistic process, and erodes faith in public discourse. This Article argues that the critics are wrong. Sponsorship disclosure law under the Communications Act of 1934 and related regulations is indeed largely ineffective, in part because the media industry has consolidated considerably and in part because the drafters could not imagine the diverse ways we create and consume media content in the twenty-first century. Congress conceived the law not only for yesterday\u27s marketplace, but also for yesterday\u27s consumer. The media consumer today is a venture consumer. Often, she knows what she wants, knows where to get it, and is aware of the risks and costs involved. The mismatch between regulators\u27 imagined consumer and the contemporary consumer means that expanded regulation of embedded advertising according to current reform proposals could end up harming consumers more than helping them. Moreover, embedded advertising is not especially amenable to effective regulation, given the incentives for artists and advertisers to collaborate in the production of entertainment content. In light of both the difficulty of correcting the regime\u27s flaws and the consumer interests threatened by expanded regulation, this Article concludes that maintaining the law as-is—rather than expanding it through the proposed reforms—better serves the consumer

Too much television?: Does watching political ads influence if and how people vote?

The goal of this study was to examine the impact of negative political advertising on a young voters’ emotions and his/her decision to vote in the next election. This was done through the lens of the theory of cultivation analysis. The theory stated that the more television a person watches, the more likely he/she is to believe what he/she sees is reality. Using a cross-sectional survey, 324 participants viewed one of four political ads or a control group ad. Although no significant evidence found that negative political ads would stop people from voting, some significant evidence suggested that negative ads demobilize voters and evoke negative emotions, which could affect their desire to vote in the next election

Selling The American People: Data, Technology, And The Calculated Transformation Of Advertising

This dissertation tells the history of a future imagined by advertisers as they interpreted and constructed the affordances of digital information technologies. It looks at how related efforts to predict and influence consumer habits and to package and sell audience attention helped orchestrate the marriage of behavioral science and big-data analytics that defines digital marketing today. My research shows how advertising and commercial media industries rebuilt their information infrastructures around electronic data processing, networked computing, and elaborate forms of quantitative analysis, beginning in the 1950s. Advertisers, agencies, and media companies accommodated their activities to increasingly calculated ways of thinking about consumers and audiences, and to more statistical and computational forms of judgement. Responding to existing priorities and challenges, and to perceived opportunities to move closer to underlying ambitions, a variety of actors envisioned the future of marketing and media through a set of possibilities that became central to the commercialization of digital communications. People involved in the television business today use the term “advanced advertising” to describe a set of abilities at the heart of internet and mobile marketing: programmability (automation), addressability (personalization), shoppability (interactive commerce), and accountability (measurement and analytics). In contrast to the perception that these are unique elements of a “new” digital media environment that emerged in the mid-1990s, I find that these themes appear conspicuously in designs for using and shaping information technologies over the course of the past six decades. I use these potential abilities as entry points for analyzing a broader shift in advertising and commercial media that began well before the popular arrival of the internet. Across the second half of the twentieth century, the advertising industry, a major cultural and economic institution, was reconstructed around the goal of expanding its abilities to account for and calculate more of social and personal life. This transformation sits at an intersection where the processing of data, the processing of commerce, and the processing of culture collide