Visual Targeted Advertisement System Based on User Profiling and Content Consumption for Mobile Broadcasting Television

Content personalisation is one of the main aims of the mobile media delivery business models, as a new way to improve the user’s experience. In broadcasting networks, the content is sent “one to many”, so a complete personalisation where the user may select any content is not possible. But using the mobile bidirectional return channel (e.g. UMTS connection) visual targeted advertising can be performed in a simple way: by off-line storing the advertisement for selectively replacing the normal broadcasted advertisement. In fact, these concepts provide powerful methods to increase the value of the service, mainly in mobile environments. In this article we present a novel intelligent content personalisation system for targeted advertising over mobile broadcasting networks and terminals, based on user profiling and clustering, as a new solution where the use of content personalisation represents the competitive advantage over traditional advertising

Screen real estate ownership based mechanism for negotiating advertisement display

As popularity of online video grows, a number of models of advertising are emerging. It is typically the brokers – usually the operators of websites – who maintain the balance between content and advertising. Existing approaches focus primarily on personalizing advertisements for viewer segments, with minimal decision-making capacity for individual viewers. We take a resource ownership view on this problem. We view consumers’ attention space, which can be abstracted as a display screen for an engaged viewer, as precious resource owned by the viewer. Viewers pay for the content they wish to view in dollars, as well as in terms of their attention. Specifically, advertisers may make partial payment for a viewer’s content, in return for receiving the viewer’s attention to their advertising. Our approach, named “FlexAdSense”, is based on CyberOrgs model, which encapsulates distributed owned resources for multi-agent computations. We build a market of viewers’ attention space in which advertisers can trade, just as viewers can trade in a market of content. We have developed key mechanisms to give viewers flexible control over the display of advertisements in real time. Specific policies needed for automated negotiations can be plugged-in. This approach relaxes the exclusivity of the relationship between advertisers and brokers, and empowers viewers, enhancing their viewing experience. This thesis presents the rationale, design, implementation, and evaluation of FlexAdSense. Feature comparison with existing advertising mechanisms shows how FlexAdSense enables viewers to control with fine-grained flexibility. Experimental results demonstrate the scalability of the approach, as the number of viewers increases. A preliminary analysis of user overhead illustrates minimal attention overhead for viewers as they customize their policies

Money for Something: Music Licensing in the 21st Century

[Excerpt] The laws that determine who pays whom in the digital world were written, by and large, at a time when music was primarily performed via radio broadcasts or distributed through physical media (such as sheet music and phonograph records), and when each of these forms of music delivery represented a distinct channel with unique characteristics. With the emergence of the Internet, Congress updated some copyright laws in the 1990s. It applied one set of legal provisions to digital services it viewed as akin to radio broadcasts and another set to digital services it viewed as akin to physical media. Since that time consumers have increasingly been consuming music via digital services that incorporate attributes of both radio and physical media. However, companies that compete in enabling consumers to access music may face very different costs to license music, depending on the technology they use and the features they offer. These differences in technology and features also affect the amount of money received by songwriters, performers, music publishers, and record companies. U.S. copyright law allows performers and record labels to collectively designate an agent to receive payments and to negotiate the licensing fees that certain types of digital music services must pay to stream music to their customers. Groups representing public radio and educational stations reached voluntary agreements with the agent, SoundExchange, in 2015. Rates paid by parties that do not reach voluntary agreements with SoundExchange during a limited negotiation period are instead set by the Copyright Royalty Board (CRB), a panel of three judges appointed by the Librarian of Congress. On December 16, 2015, the CRB set rates for online music streaming services for the period 2016 through 2020. For nonsubscription services, the CRB reduced the per-stream rate it had set in the previous rate proceeding, but the costs paid by several “small” music streaming services are likely to increase. Advocates of the small streaming services have launched a petition asking Congress to either allow their previous agreements to continue indefinitely or discontinue the requirement that small streaming services pay royalties to performers and record labels. SoundExchange has objected that the rates set by the CRB do not provide adequate compensation to performers and record labels. Members have introduced several bills in the 114th Congress that would change the amounts various participants in the music industry pay or receive in royalties. These bills are controversial, as they could alter the cost structures and revenues of broadcast radio stations, songwriters, performers, and others at a time when the music industry’s overall revenues are not growing. At the same time, the U.S. Department of Justice (DOJ) is continuing a review of consent decrees it entered into with music publishers in the 1940s. The outcome could affect the extent to which songwriters can control the use of their works

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

User generated content for IMS-based IPTV

Includes abstract.Includes bibliographical references.Web 2.0 services have been on the rise due to improved bandwidth availability. Users can now connect to the internet with a variety of portable devices which are capable of performing multiple tasks. Due to this, services such as Voice over IP (VoIP), presence, social networks, instant messaging (IM) and Internet Protocol television (IPTV) to mention but a few, started to emerge...This thesis proposed a framework that will offer user-generated content on an IMS-Based IPTV and the framework will include a personalised advertising system..

Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning

Variances in ad impression outcomes across demographic groups are increasingly considered to be potentially indicative of algorithmic bias in personalized ads systems. While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems. VRS seeks to achieve a distribution of impressions with respect to selected protected class (PC) attributes that more closely aligns the demographics of an ad's eligible audience (a function of advertiser targeting criteria) with the audience who sees that ad, in a privacy-preserving manner. We first define metrics to quantify fairness gaps in terms of ad impression variances with respect to PC attributes including gender and estimated race. We then present the VRS for re-ranking ads in an impression variance-aware manner. We evaluate VRS via extensive simulations over different parameter choices and study the effect of the VRS on the chosen fairness metric. We finally present online A/B testing results from applying VRS to Meta's ads systems, concluding with a discussion of future work. We have deployed the VRS to all users in the US for housing ads, resulting in significant improvement in our fairness metric. VRS is the first large-scale deployed framework for pursuing fairness for multiple PC attributes in online advertising.Comment: 11 pages, 7 figure, KDD 202

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving