This paper describes a novel content-based image recommendation system based on new image low level descriptors derived from the well known MPEG-7 parameters. Furthermore, it also proposes the integration of this recommendation system into a content-aware network architecture to enhance and enrich the content delivery and improve user's experience.
One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user.
Sentiment analysis is an active topic in Natural Language Processing (NLP). It has attracted a significant interest of research community due to the wide range of applications, including social-media, fake news spotting and interactive applications. In this paper, we present a novel approach for semiautomatic background creation and conspiracy classification. For this purpose, a complete framework including novel recurrent models is proposed. The BORJIS: Best algorithm foR Joint conspiracy and sarcasm detection has been tested on twitter-crawled data and It is composed by: (a) the crawler and labelling module, (b) the features vector extraction and (c) the conspiracy classifier. BORJIS is established as a novel approach for processing variable length inputs to detect conspiracy. Both the data and the code are referenced in the article.
Content personalization provides a clear economical advantage over traditional content delivery since it contributes to increase the value of a given media service. This paper describes a new intelligent system for content flow personalization over mobile television environments.
Universal access on equal terms to audiovisual content is a key point for the full inclusion of people with disabilities in activities of daily life. As a real challenge for the current Information Society, it has been detected but not achieved in an efficient way, due to the fact that current access solutions are mainly based in the traditional television standard and other not automated high-cost solutions. The arrival of new technologies within the hybrid television environment together with the application of different artificial intelligence techniques over the content will assure the deployment of innovative solutions for enhancing the user experience for all. In this paper, a set of different tools for image enhancement based on the combination between deep learning and computer vision algorithms will be presented. These tools will provide automatic descriptive information of the media content based on face detection for magnification and character identification. The fusion of this information will be finally used to provide a customizable description of the visual information with the aim of improving the accessibility level of the content, allowing an efficient and reduced cost solution for all.
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