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

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

Individualisation avancée des services IPTV

Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type nTS (pour network Time Shifting en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type nPVR (pour network Personal Video Recorder en anglais) qui permettent d enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l utilisateur. L adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l utilisateur et aussi des informations contextesInternet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users devices screen types, size, supported resolution, and networks available network types to be used by the user, available bandwidth, .. ) as well as the context of the service itself (content type and description, available format HD/SD , available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user s environment (devices and networks status), interpreting user s requirements and making the user s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users preferences) which in turn highly improves the users Quality of Experience (QoE) and enriching the offers of IPTV servicesEVRY-INT (912282302) / SudocSudocFranceF

Implicit Social Networking: Discovery of Hidden Relationships, Roles and Communities among Consumers

AbstractThis paper proposes the implicit social networking as an innovative methodology for approaching consumers who possess information-rich user profiles based on aplethora of online services they use. An implicit social network is not explicitly built by consumers themselves, but implicitly calculated by third parties based on a level of a common interest between consumers (i.e., profile matchmaking). The analysis of aconsumer social network created in such a manner enables discovery of hidden roles, relationships and communities among consumers and represents a basis for provisioning of innovative services (e.g., personalized and/or context-aware services such as recommender systems). The implicit social networking methodology is evaluated through two pilot cases: (i) implicit social networking based on the SmartSocial platform; and (ii) implicit social networking of IPTV users. The generalizability of the implicit social networking is demonstrated through additional example aimed not at external company stakeholders (e.g., company consumers), but at internal stakeholders (i.e., company employees) through the implicit corporate social networking pilot case

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

Impact of Social Media on TV Content Consumption: New Market Strategies, Scenarios and Trends

The mass adoption of Social Media together with the proliferation and widely usage of multi-connected companion devices have tremendously transformed the TV/video consumption paradigm, opening the door to a new range of possibilities. This Special Issue has aimed at analyzing, from different point of views, the impact of Social Media and social interaction tools on the TV/video consumption area. The targeted topics of this Special Issue and a general overview of the accepted articles are provided in this Guest Editorial

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

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

Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

Deliverable D9.1.1 Annual Project Scientific Report

This document comprises the publishable excerpts of the first periodic scientific report of LinkedTV. It includes a short summary, a progress report as well as a management report for the first reporting period