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

Dataretrieving for varied in different Composition Databases using Content aggregation

Keeping in mind with a variety of content choices, consumers are exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumers� demand for such diverse content, multimedia content aggregators (CAs) haveemerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict whattype of content each of its consumers prefers in a certain context,and adapt these predictions to the evolving consumers preferences, contexts, and content characteristics This paper addressesgenerate text based file data sets, such as word, text files, image file data sets, and video file data sets, It also extract data from multiple databases, evaluate user preference based query, reduce time complexity by clustering data, and increase fetching speed by using query classification

Privacy-preserving targeted advertising scheme for IPTV using the cloud

Targeted advertising is an emerging business area that provides effective services for advertisers and end users. Advertising agencies need information about users to send them targeted advertisements. However, uncontrolled access to users' sensitive information for advertising purposes violates the privacy of individuals. Therefore, efficient techniques must be used to not only preserve users privacy but also enable advertisers to reach right users. In this thesis, 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 time, which are published on the cloud server periodically along with anonymized demographics (e.g. weekly). 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 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

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

FamTV : an architecture for presence-aware personalized television

Since the advent of the digital era, the traditional TV scenario has rapidly evolved towards an ecosystem comprised of a myriad of services, applications, channels, and contents. As a direct consequence, the amount of available information and configuration options targeted at today's end consumers have become unmanageable. Thus, personalization and usability emerge as indispensable elements to improve our content-overloaded digital homes. With these requirements in mind, we present a way to combine content adaptation paradigms together with presence detection in order to allow a seamless and personalized entertainment experience when watching TV.This work has been partially supported by the Community of Madrid (CAM), Spain under the contract number S2009/TIC-1650.Publicad

An architecture for evolving the electronic programme guide for online viewing

Watching television and video content is changing towards online viewing due to the proliferation of content providers and the prevalence of high speed broadband. This trend is coupled to an acceleration in the move to watching content using non-traditional viewing devices such as laptops, tablets and smart phones. This, in turn, poses a problem for the viewer in that it is becoming increasingly difficult to locate those programmes of interest across such a broad range of providers. In this thesis, an architecture of a generic cloud-based Electronic Programme Guide (EPG) system has been developed to meet this challenge. The key feature of this architecture is the way in which it can access content from all of the available online content providers and be personalized depending on the viewer’s preferences and interests, viewing device, internet connection speed and their social network interactions. Fundamental to its operation is the translation of programme metadata adopted by each provider into a unified format that is used within the core system. This approach ensures that the architecture is extensible, being able to accommodate any new online content provider through the addition of a small tailored search agent module. The EPG system takes the programme as its core focus and provides a single list of recommendations to each user regardless of their origins. A prototype has been developed in order to validate the proposed system and evaluate its operation. Results have been obtained through a series of user trials to assess the system’s effectiveness in being able to extract content from several sources and to produce a list of recommendations which match the user’s preferences and context. Results show that the EPG is able to offer users a single interface to online television and video content providers and that its integration with social networks ensures that the recommendation process is able to match or exceed the published results from comparable, but more constrained, systems

CHORUS Deliverable 3.3: Vision Document - Intermediate version

The goal of the CHORUS vision document is to create a high level vision on audio-visual search engines in order to give guidance to the future R&D work in this area (in line with the mandate of CHORUS as a Coordination Action). This current intermediate draft of the CHORUS vision document (D3.3) is based on the previous CHORUS vision documents D3.1 to D3.2 and on the results of the six CHORUS Think-Tank meetings held in March, September and November 2007 as well as in April, July and October 2008, and on the feedback from other CHORUS events. The outcome of the six Think-Thank meetings will not just be to the benefit of the participants which are stakeholders and experts from academia and industry – CHORUS, as a coordination action of the EC, will feed back the findings (see Summary) to the projects under its purview and, via its website, to the whole community working in the domain of AV content search. A few subjections of this deliverable are to be completed after the eights (and presumably last) Think-Tank meeting in spring 2009