Rebeca P. Díaz-Redondo

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.

Provision of distance learning services over Interactive Digital TV with MHP

E-learning technologies have developed greatly in recent years, with considerable success. However, there is increasing evidence that web-based learning is not reaching the social sectors which are more reluctant to contact with the new technologies, thus leading to inequalities in the access to education and knowledge in the Information Society. By hiding the intricacies of computers behind the familiarity of household equipment, Interactive Digital TV (IDTV) is considered to play a key role in addressing this problem, and the term t-learning has been recently coined to mean TV-based interactive learning. Despite several approaches to t-learning have been proposed, works are missing that conceive it as a whole, delimit its scope in comparison with web-based learning and analyze the influence of the normalization of IDTV as a services platform. This paper addresses these issues, and introduces a framework for the development and deployment of t-learning services that promotes interoperability and reuse while taking into account the characteristic features of the IDTV medium.

An extension to the ADL SCORM standard to support adaptivity: The t-learning case-study

Current e-learning standards have been designed to provide reusability and interoperability. Besides these features, content personalisation is also necessary, although current standards do not fully support it. In this paper, we study the adaptation possibilities of the SCORM standard and present an extension to permit adaptivity according to users characteristics. It comprises a syntax for adaptivity rules based on a set of adaptation parameters. The actual values of these adaptation parameters are deduced from the user profile, using inference rules. As a result, adaptive courses are obtained, created with the aim of being personalised before shown to the student.

T-MAESTRO and its authoring tool: using adaptation to integrate entertainment into personalized t-learning

Interactive Digital TV opens new learning possibilities where new forms of education are needed. On the one hand, the combination of education and entertainment is essential to boost the participation of viewers in TV learning (t-learning), overcoming their typical passiveness. On the other hand, researchers broadly agree that in order to prevent the learner from abandoning the learning experience, it is necessary to take into account his/her particular needs and preferences by means of a personalized experience. Bearing this in mind, this paper introduces a new approach to the conception of personalized t-learning: edutainment and entercation experiences. These experiences combine TV programs and learning contents in a personalized way, with the aim of using the playful nature of TV to make learning more attractive and to engage TV viewers in learning. This paper brings together our work in constructing edutainment/entercation experiences by relating TV and learning contents. Taking personalization one step further, we propose the adaptation of learning contents by defining A-SCORM (Adaptive-SCORM), an extension of the ADL SCORM standard. Over and above the adaptive add-ons, this paper focuses on two fundamental entities for the proposal: (1) an Intelligent Tutoring System, called T-MAESTRO, which constructs the t-learning experiences by applying semantic knowledge about the t-learners; and (2) the authoring tool which allow teachers to create adaptive courses with a minimal technical background.

AVATAR: An Advanced Multi-Agent Recommender System of Personalized TV Contents by Semantic Reasoning

In this paper a recommender system of personalized TV contents, named AVATAR, is presented. We propose a modular multi-agent architecture for the system, whose main novelty is the semantic reasoning about user preferences and historical logs, to improve the traditional syntactic content search. Our approach uses Semantic Web technologies – more specifically an OWL ontology – and the TV-Anytime standard to describe the TV contents. To reason about the ontology, we have defined a query language, named LIKO, for inferring knowledge from properties contained in it. In addition, we show an example of a semantic recommendation by means of some LIKO operators.

Receiver-side semantic reasoning for digital TV personalization in the absence of return channels

Experience has proved that interactive applications delivered through Digital TV must provide personalized information to the viewers in order to be perceived as a valuable service. Due to the limited computational power of DTV receivers (either domestic set-top boxes or mobile devices), most of the existing systems have opted to place the personalization engines in dedicated servers, assuming that a return channel is always available for bidirectional communication. However, in a domain where most of the information is transmitted through broadcast, there are still many cases of intermittent, sporadic or null access to a return channel. In such situations, it is impossible for the servers to learn who is watching TV at the moment, and so the personalization features become unavailable. To solve this problem without sacrificing much personalization quality, this paper introduces solutions to run a downsized semantic reasoning process in the DTV receivers, supported by a pre-selection of material driven by audience stereotypes in the head-end. Evaluation results are presented to prove the feasibility of this approach, and also to assess the quality it achieves in comparison with previous ones.

A Six-valued Logic to Reason about Uncertainty and Inconsistency in Requirements Specifications

The development of requirements specifications is characterized by the uncertain and changeable knowledge available about the systems to be built. This paper presents a many-valued logic that enables effective reasoning about uncertainty and inconsistency in requirements specifications, motivating the election of six truth values and the definition of a new implication connective. The adequacy of this logic to support a formal development methodology is assessed through a comparison with Belnaps four-valued logic in combination with the classical implications.

Extending SCORM to create adaptive courses

Current e-learning standards have been designed to provide reusability of educational contents and interoperability between systems. Besides these features, content personalization is also necessary, although current standards do not fully support it. In this paper, the ADL SCORM (Sharable Content Object Reference Model) adaptation possibilities are studied and an extension to this standard is presented in an effort to create adaptive courses that should be personalized before shown to the student.

A model for personalized learning through IDTV

Interactive Digital TV (IDTV) opens new learning possibilities where new forms of education are needed. In this paper we explain a new conception of t-learning experiences where TV programs and learning contents are combined. In order for its creation to be possible we will use Adaptive Hypermedia techniques and Semantic Reasoning to design an Intelligent Tutoring System (ITS) whose tasks consist in selecting, combining and personalizing the contents to construct these learning experiences.

A Mixed XML-JavaBeans Approach to Developing T-learning Applications for the Multimedia Home Platform

E-learning technologies have developed greatly in recent years, with considerable success, which has suggested extending distance education to other mediums. This paper studies the possibilities of Interactive Digital TV to provide educational services (t-learning) and analyzes the support offered by the Multimedia Home Platform standard (MHP). We also present an approach to developing interactive t-learning courses and a tool, based on public and well-known technologies, that implements our proposal over the MHP technological framework. Our approach is remarkable for being flexible, extensible and easy to integrate with existing standards for the management of learning content, thus promoting interoperability and content reuse. In addition, applications can be developed with no need of programming knowledge. This is essential to free designers from technological details, so that they can concentrate on the broadcast contents, their sequence, interrelations and every aspect that makes up a value-added application.