A Survey on Personalized Multimedia Content Search

Millions of users share opinions on different aspects of life every day. User can give opinions through their comments related to multimedia contents. For analyzing user’s demands we can evaluate user comments as a common online resource. Comments given by user provide clues about them & also their interest area. In this paper, we done survey on different techniques used for personalized search in relevant information retrieval. Recommender systems or recommendation systems are a subclass of information filtering system that seek to predict ‘rating’ or ‘preference’ that are given by users. We propose a personalized search system, called MovieMine, enhance this proposed system to provide more relevant data summarized according to past & present comments left by user. Large movie data center provide movie review which can be utilized for more specific output. Experimental evaluations show that our proposed techniques are efficient and perform better than previously proposed methods. As in research work we can study more types of sentiment analysis with different recommender systems approach. DOI: 10.17762/ijritcc2321-8169.15024

Combining Content with User Preferences for Non-Fiction Multimedia Recommendation: A Study on TED Lectures

This paper introduces a new dataset and compares several methods for the recommendation of non-fiction audio visual material, namely lectures from the TED website. The TED dataset contains 1,149 talks and 69,023 profiles of users, who have made more than 100,000 ratings and 200,000 comments. The corresponding metadata, which we make available, can be used for training and testing generic or personalized recommender systems. We define content-based, collaborative, and methods (LSI, LDA, RP, and ESA). We compare these methods on a personalized recommendation task in two settings, a cold-start and a non-cold-start one. In the cold-start setting, semantic vector spaces perform better than keywords. In the non-cold-start setting, where collaborative information can be exploited, content-based methods are outperformed by collaborative filtering ones, but the proposed combined method shows acceptable performances, and can be used in both settings. For the generic recommendation task, LSI and RP again outperform TF-IDF

Modelado de sistemas multimedia para personalización y recomendación híbrida a partir del consumo audiovisual de los usuarios

This doctoral thesis focuses on the modeling of multimedia systems to create personalized recommendation services based on the analysis of users’ audiovisual consumption. Research is focused on the characterization of both users’ audiovisual consumption and content, specifically images and video. This double characterization converges into a hybrid recommendation algorithm, adapted to different application scenarios covering different specificities and constraints. Hybrid recommendation systems use both content and user information as input data, applying the knowledge from the analysis of these data as the initial step to feed the algorithms in order to generate personalized recommendations. Regarding the user information, this doctoral thesis focuses on the analysis of audiovisual consumption to infer implicitly acquired preferences. The inference process is based on a new probabilistic model proposed in the text. This model takes into account qualitative and quantitative consumption factors on the one hand, and external factors such as zapping factor or company factor on the other. As for content information, this research focuses on the modeling of descriptors and aesthetic characteristics, which influence the user and are thus useful for the recommendation system. Similarly, the automatic extraction of these descriptors from the audiovisual piece without excessive computational cost has been considered a priority, in order to ensure applicability to different real scenarios. Finally, a new content-based recommendation algorithm has been created from the previously acquired information, i.e. user preferences and content descriptors. This algorithm has been hybridized with a collaborative filtering algorithm obtained from the current state of the art, so as to compare the efficiency of this hybrid recommender with the individual techniques of recommendation (different hybridization techniques of the state of the art have been studied for suitability). The content-based recommendation focuses on the influence of the aesthetic characteristics on the users. The heterogeneity of the possible users of these kinds of systems calls for the use of different criteria and attributes to create effective recommendations. Therefore, the proposed algorithm is adaptable to different perceptions producing a dynamic representation of preferences to obtain personalized recommendations for each user of the system. The hypotheses of this doctoral thesis have been validated by conducting a set of tests with real users, or by querying a database containing user preferences - available to the scientific community. This thesis is structured based on the different research and validation methodologies of the techniques involved. In the three central chapters the state of the art is studied and the developed algorithms and models are validated via self-designed tests. It should be noted that some of these tests are incremental and confirm the validation of previously discussed techniques. Resumen Esta tesis doctoral se centra en el modelado de sistemas multimedia para la creación de servicios personalizados de recomendación a partir del análisis de la actividad de consumo audiovisual de los usuarios. La investigación se focaliza en la caracterización tanto del consumo audiovisual del usuario como de la naturaleza de los contenidos, concretamente imágenes y vídeos. Esta doble caracterización de usuarios y contenidos confluye en un algoritmo de recomendación híbrido que se adapta a distintos escenarios de aplicación, cada uno de ellos con distintas peculiaridades y restricciones. Todo sistema de recomendación híbrido toma como datos de partida tanto información del usuario como del contenido, y utiliza este conocimiento como entrada para algoritmos que permiten generar recomendaciones personalizadas. Por la parte de la información del usuario, la tesis se centra en el análisis del consumo audiovisual para inferir preferencias que, por lo tanto, se adquieren de manera implícita. Para ello, se ha propuesto un nuevo modelo probabilístico que tiene en cuenta factores de consumo tanto cuantitativos como cualitativos, así como otros factores de contorno, como el factor de zapping o el factor de compañía, que condicionan la incertidumbre de la inferencia. En cuanto a la información del contenido, la investigación se ha centrado en la definición de descriptores de carácter estético y morfológico que resultan influyentes en el usuario y que, por lo tanto, son útiles para la recomendación. Del mismo modo, se ha considerado una prioridad que estos descriptores se puedan extraer automáticamente de un contenido sin exigir grandes requisitos computacionales y, de tal forma que se garantice la posibilidad de aplicación a escenarios reales de diverso tipo. Por último, explotando la información de preferencias del usuario y de descripción de los contenidos ya obtenida, se ha creado un nuevo algoritmo de recomendación basado en contenido. Este algoritmo se cruza con un algoritmo de filtrado colaborativo de referencia en el estado del arte, de tal manera que se compara la eficiencia de este recomendador híbrido (donde se ha investigado la idoneidad de las diferentes técnicas de hibridación del estado del arte) con cada una de las técnicas individuales de recomendación. El algoritmo de recomendación basado en contenido que se ha creado se centra en las posibilidades de la influencia de factores estéticos en los usuarios, teniendo en cuenta que la heterogeneidad del conjunto de usuarios provoca que los criterios y atributos que condicionan las preferencias de cada individuo sean diferentes. Por lo tanto, el algoritmo se adapta a las diferentes percepciones y articula una metodología dinámica de representación de las preferencias que permite obtener recomendaciones personalizadas, únicas para cada usuario del sistema. Todas las hipótesis de la tesis han sido debidamente validadas mediante la realización de pruebas con usuarios reales o con bases de datos de preferencias de usuarios que están a disposición de la comunidad científica. La diferente metodología de investigación y validación de cada una de las técnicas abordadas condiciona la estructura de la tesis, de tal manera que los tres capítulos centrales se estructuran sobre su propio estudio del estado del arte y los algoritmos y modelos desarrollados se validan mediante pruebas autónomas, sin impedir que, en algún caso, las pruebas sean incrementales y ratifiquen la validación de técnicas expuestas anteriormente

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

TVCOMmunity : arquiteturas, avaliação, contextos educativos

Doutoramento em Multimédia em EducaçãoA Televisão tem tido um contributo na aprendizagem do telespetador desde a segunda metade do século passado, quer em contextos formais de ensinoaprendizagem, quer em contextos não formais aliando os conteúdos educativos ao entretenimento e à informação. A Internet, por sua vez, tem contribuído para um maior e mais rápido acesso à informação e a possibilidade de colaborar na criação de uma inteligência coletiva. A resistência dos estudantes na utilização dos mecanismos e ferramentas de suporte à aprendizagem utilizados atualmente provocou a necessidade de desenvolver uma aplicação que pudesse responder a esse desafio. Nesse sentido, este trabalho, apresenta, a prototipagem, avaliação e aplicação em contexto educativo, de uma plataforma de vídeo interativa em ambiente Web para multidispositivos (e.g. televisor, computador, dispositivos móveis). Numa primeira instância é apresentado o desenvolvimento das arquiteturas e ambientes da aplicação TV.COMmunity onde são detalhadas as funcionalidades de enriquecimento de conteúdo introduzidas. Passando este protótipo a uma segunda fase onde é avaliado por Especialistas em Tecnologias Educativas e Estudantes de Educação e Comunicação Multimédia através de testes de usabilidade, divididos em três sessões. E por fim, após avaliado e reformulado, o protótipo é aplicado, através de testes de user experience, a dois contextos educativos. O primeiro, com a duração de um semestre, em que estudantes de Mestrado (profissionais multimédia e professores), divididos em grupos, interagem com o protótipo desenvolvendo conteúdos educativos para este. O segundo, com a duração de quatro semanas, consiste na realização de um Massive Open Online Course (MOOC), com recurso à aplicação TV.COMmunity, por três turmas de licenciatura da área dos multimédia. Com estes testes pretende-se analisar os diferentes modos de interação (de professores e estudantes) na avaliação de uma plataforma de vídeo interativa, perceber quais as funcionalidades que esta deve integrar para responder às necessidades destes agentes e identificar de que modo a aplicação pode ser implementada e disponibilizada à comunidade. As principais conclusões do estudo apontam diferentes comportamentos, por parte de professores e estudantes, sendo os primeiros mais ativos e exigentes e os estudantes mais passivos e com maiores níveis de satisfação, na avaliação do protótipo. Em termos de necessidades de funcionalidades a integrar, as exigências são idênticas aos diferentes grupos, destaque para o enriquecimento de conteúdos (ferramenta que permite incorporar outros conteúdos sobre vídeo), divisão por capítulos e ainda a possibilidade de interação por texto, seja esta de modo privado (notas) ou público (comentários), com a particularidade de todas estas opções estarem ligadas a um momento específico do vídeo. Em termos de implementação numa instituição esta plataforma é de fácil integração e permite utilizar os recursos da cloud (nuvem), poupando espaço nos servidores.The television has had a significant contribution in viewers’ learning since the second half of the last century, whether in formal teaching and learning contexts, or in non-formal contexts, combining educational content with entertainment and information. The Internet, in turn, has contributed to a greater and faster access to information and enabled the cooperation in order to create a collective intelligence. The resistance of the students in the use of learning support tools and mechanisms currently used triggered the need to develop software that could address this challenge. Therefore, this work presents an interactive video platform (in a Web environment) for multidevice (e.g. TV, computer, mobile devices) in its different components: the prototyping, evaluation and application in an educational context. In a first moment it presents the development of architectures and environments of the TV.COMmunity application where the content enrichment capabilities introduced are detailed. Afterwards this prototype is evaluated by experts in Educational Technologies and students from the Education and Multimedia Communication field, through usability testing, divided into three sessions. Finally, once the prototype has been evaluated and redesigned, it is applied in two educational settings through user experience testing. The first test, which lasts one semester, consists of the interaction and development of the prototype’ contents by MA students (multimedia professionals and teachers), divided into groups. In the second test, which lasts four weeks, three BA classes of multimedia students undertake a Massive Open Online Course (MOOC) using the TV.COMmunity application. The aim of these tests is to ascertain the different interaction modes (by teachers and students) in the evaluation of an interactive video platform, to understand which features it should integrate to meet the needs of these agents and to identify how the application can be implemented and made available to the community. The key findings of the present study point to different behaviours of teachers and students; the first group is more active and demanding and the second group is more passive and with higher levels of satisfaction regarding the prototype’s evaluation. In what concerns functionalities to integrate, the requirements are identical for both groups, with emphasis on the content enrichment (tool that lets us incorporate other content over the video), chapters division and also the possibility of interaction by text, whether in a private setting (notes) or in a public one (reviews), with the particularity that all of these options are linked to a specific time of the video. In terms of implementation in an institution, this platform is easy to integrate and allows using the resources of the cloud, saving space on the servers