Interactive TV meets Mobile Computing

The talk presents some recent developments in interactive digital television and discusses the trends and challenges of bringing TV services to mobile devices. Two areas will be addressed: portable use of mobile devices to complement the TV set in the home and mobile TV services while on the move using mobile broadcasting technology

Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

Personalised Dynamic Viewer Profiling for Streamed Data

Nowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.info:eu-repo/semantics/publishedVersio

Mars: a personalised mobile activity recognition system

Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today’s smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources

Visual collaging of music in a digital library

This article explores the role visual browsing can play within a digital music library. The context to the work is provided through a review of related techniques drawn from the fields of digital libraries and human computer interaction. Implemented within the open source digital library toolkit Greenstone, a prototype system is described that combines images located through textual metadata with a visualisation technique known as collaging to provide a leisurely, undirected interaction with a music collection. Emphasis in the article is given to the augmentations of the basic technique to work in the musical domain

Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms.info:eu-repo/semantics/publishedVersio

User evaluation outside the lab: the trial of Físchlár-News

A user study of Físchlár-News system was conducted in Spring 2004 with 16 users, each user using the system for a 1-month period. Físchlár-News is an experimental online news archive that incorporates various automatic content-based video indexing techniques and a news story recommender algorithm to process and index the daily 9 o’clock broadcast news from TV and allows its users to browse, search, be recommended, and play news stories on a conventional web browser. Pre and post-trial questionnaires, interaction logging and incident diary methods collected both qualitative and quantitative usage data during the trial period. While the details of the findings from this evaluation is reported elsewhere, in this paper we report the details of the methodology taken and our experience of conducting this evaluation

Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network

Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part of this thesis, the importance and role of personalisation in future mobile networks is studied. This is followed, by an agent based futuristic user scenario that addresses the provision of rich data services independent of location. Scenario analysis identifies the requirements and challenges to be solved for the realisation of a personalised service. An architecture based on IP Multimedia Subsystem is proposed for mobility and to provide service continuity whilst roaming between two different access standards. Another aspect of personalisation, which is user preference modelling, is investigated in the context of service selection in a multi 3rd party service provider environment. A model is proposed for the automatic acquisition of user preferences to assist in service selection decision-making. User preferences are modelled based on a two-level Bayesian Metanetwork. Personal agents incorporating the proposed model provide answers to preference related queries such as cost, QoS and service provider reputation. This allows users to have their preferences considered automatically