An enhanced content selection mechanism for personalization of video news programmes

In this paper, we propose a content selection framework that improves the users` experience when they are enriching or authoring pieces of news. This framework combines a variety of techniques to retrieve semantically related videos, based on a set of criteria which are specified automatically depending on the media`s constraints. The combination of different content selection mechanisms can improve the quality of the retrieved scenes, because each technique`s limitations are minimized by other techniques` strengths. We present an evaluation based on a number of experiments, which show that the retrieved results are better when all criteria are used at time.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Taking advantage of contextualized interactions while users watch TV

While watching TV, viewers use the remote control to turn the TV set on and off, change channel and volume, to adjust the image and audio settings, etc. Worldwide, research institutes collect information about audience measurement, which can also be used to provide personalization and recommendation services, among others. The interactive digital TV offers viewers the opportunity to interact with interactive applications associated with the broadcast program. Interactive TV infrastructure supports the capture of the user-TV interaction at fine-grained levels. In this paper we propose the capture of all the user interaction with a TV remote control-including short term and instant interactions: we argue that the corresponding captured information can be used to create content pervasively and automatically, and that this content can be used by a wide variety of services, such as audience measurement, personalization and recommendation services. The capture of fine grained data about instant and interval-based interactions also allows the underlying infrastructure to offer services at the same scale, such as annotation services and adaptative applications. We present the main modules of an infrastructure for TV-based services, along with a detailed example of a document used to record the user-remote control interaction. Our approach is evaluated by means of a proof-of-concept prototype which uses the Brazilian Digital TV System, the Ginga-NCL middleware.