Manuel Ramos-Cabrer

AVATAR: an improved solution for personalized TV based on semantic inference

The generalized arrival of the digital TV will bring a significant increase in the amount of channels and programs available to end users, with many more difficulties for them to find interesting programs among a myriad of irrelevant contents. Thus, automatic content recommenders should receive special attention in the following years to improve the assistance to users. Current approaches of content recommenders have important well-known deficiencies, which difficult their wide acceptance. In this paper, a new approach for automatic content recommendation is presented, based on the so-called semantic Web technologies, that significantly reduces those deficiencies. The approach has been implemented in the AVATAR tool, a hybrid content recommender that makes extensive use of well-known standards, such as MHP, TV-anytime, or OWL.

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.

Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems

Recommender systems arose in view of the information overload present in numerous domains. The so-called content-based recommenders offer products similar to those the users liked in the past. However, due to the use of syntactic similarity metrics, these systems elaborate overspecialized recommendations including products very similar to those the user already knows. In this paper, we present a strategy that overcomes overspecialization by applying reasoning techniques borrowed from the semantic Web. Thanks to the reasoning, our strategy discovers a huge amount of knowledge about the users preferences, and compares them with available products in a more flexible way, beyond the conventional syntactic metrics. Our reasoning-based strategy has been implemented in a recommender system for interactive digital television, with which we checked that the proposed technique offers accurate enhanced suggestions that would go unnoticed in the traditional approaches.

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.

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems☆

Abstract Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.

MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning

In an increasingly competitive market, stakeholders of the television industry strive to exploit all the possibilities to get revenues from advertising, but their practices are usually at odds with the comfort of the TV viewers. This paper presents the proof of concept of MiSPOT, a system that brings a non-invasive and fully personalized form of advertising to Interactive Digital TV, targeting both domestic and mobile receivers. MiSPOT employs semantic reasoning techniques to select advertisements suited to the preferences, interests and needs of each individual viewer, and then relies on multimedia composition abilities to blend the advertising material with the TV program he/she is viewing at any time. The advertisements can be set to launch interactive commercials, thus enabling means for the provision of t-commerce services. Evaluation experiments are described to show the technical viability of the proposal, and also to gauge the opinions of end users. Questions about the potential impact and exploitation of this new form of advertising are addressed too.

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.