Yolanda Blanco-Fernández

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.

TV program recommendation for groups based on muldimensional TV-anytime classifications

The advent of Digital TV and Personal Digital Recorders promise to change the way people watch TV. The higher efficiency of digital coding will lead to increasing the number of contents offered to the user, demanding automatic tools for content recommendation. In the other hand, digital recorders will permit a non-linear consumption model, enabling the creation of (automatic) personalized schedules that combine the appealing contents for a specific user or group of users. This paper presents an approach to content recommendation for groups of people, based on TV-Anytime descriptions of TV contents and semantic reasoning techniques.

An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles

Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/her particular preferences evolve over time. This adaptation process typically considers that a users interest in a given type of product always decreases with time from the moment of the last purchase. However, the necessity of a product for a user depends on both the nature of the own item and the personal preferences of the user, being even possible that his/her interest increases over time from the purchase. Some existing approaches focus only on the first factor, missing the point that the influence of time can be very different for different users. To solve this limitation, we present a filtering strategy that exploits the semantics formalized in an ontology in order to link items (and their features) to time functions. The novelty lies within the fact that the shapes of these functions are corrected by temporal curves built from the consumption stereotypes into which each user fits best. Our preliminary experiments involving real users have revealed significant improvements of recommendation precision with regard to previous time-driven filtering 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.

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.

Monitoring medicine intake in the networked home: The iCabiNET solution

We introduce an intelligent medicine cabinet as a new element of a residential network, acting as a secure place to store sensitive health information, and therefrom access a range of interactive health care applications. This paper describes the functionalities related to monitoring the intake of prescription and over-the-counter drugs, harnessing recent advances in smart medicine packaging and home networking. Compared to previous systems, ours helps reducing the risk of medicine misuse, featuring higher precision and enhanced interactive facilities that reach in and out of home. This contributes to solving a problem that impinges heavily on the well-being of people and the economics of public health systems.