Ivar Solheim

Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

Existing context-aware applications are limited in their support of user personalisation. Nevertheless, the increase in the use of context-aware technologies has sparked the growth in assistive applications resulting in a need to enable adaptation to reflect the changes in user behaviours. This paper introduces a systematic approach to service personalisation for mobile users in pervasive environments and presents a service-oriented distributed system architecture. The developed approach makes use of semantic technologies for user modelling and personalisation reasoning. In the paper we characterise user behaviours and needs in pervasive environments upon which ontological user models are created with special emphasis being placed on ontological modelling of dynamic and adaptive user profiles. We develop a rule-based personalisation mechanism that exploits semantic web rule mark-up language for rule design and a combination of semantic and rule-based reasoning for personalisation. We use two case studies focusing on providing personalised travel assistance for people using Help-on-Demand services deployed on a smart-phone to contextualise the discussions within the paper. The proposed approach is implemented in a prototype system, which includes Help-on-Demand services, content management services, user models and personalisation mechanisms in addition to application specific rules. Experiments have been designed and conducted to test and evaluate the approach with initial results demonstrating the functionality of the approach.

A user profile ontology based approach for assisting people with dementia in mobile environments

Personalization and context-aware applications have attracted increasing amounts of attention over recent years due to the emergence of pervasive computing applications. Nevertheless, it still remains a challenge to meet the needs of users while they are on the move. This paper introduces a novel approach for providing personalized, context-aware assistance services for users in mobile environments. Central to the approach is the use of ontological user profile modeling which captures various characteristics of a user in order to create a unique set of profile information. In addition, user profiles can adapt to changing user behavior, thus enabling services to respond to evolving user needs and preferences. We describe the overall system architecture of the proposed approach with special emphasis being placed on the user profile modelling and its expected utility based on a typical use case scenario, i.e., using a smart-phone to address the problem of the outdoor mobility of a person with Dementia. A prototype based on the Android OS is used to illustrate the application. The use of everyday technology for a real world problem highlights the potential and utility of our approach.

A Conceptual Framework for Supporting Adaptive Personalized Help-on-Demand Services

Mobile applications that encompass personalization and context-aware components are increasingly becoming more prevalent. The ability to offer personalized content and User Interfaces to the users of these applications, however, has still not been fully addressed. In this paper we describe a conceptual framework that establishes a User Profile and aims to monitor the usage patterns of users of a mobile application and, based on these patterns, provide both personalized, context aware content and user interfaces. The framework consists of four components that together contribute towards an overall Help on Demand service that is targeted at older age Smartphone users. A usage scenario is presented to describe the typical usage of the help on demand service.

Mining usage data for adaptive personalisation of smartphone based help-on-demand services

Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively.

Learning Behaviour for Service Personalisation and Adaptation

Context-aware applications within pervasive environments are increasingly being developed as services and deployed in the cloud. As such these services are increasingly required to be adaptive to individual users to meet their specific needs or to reflect the changes of their behavior. To address this emerging challenge this paper introduces a service-oriented personalisation framework for service personalisation with special emphasis being placed on behavior learning for user model and service function adaptation. The paper describes the system architecture and the underlying methods and technologies including modelling and reasoning, behavior analysis and a personalisation mechanism. The approach has been implemented in a service-oriented prototype system, and evaluated in a typical scenario of providing personalised travel assistance for the elderly using the help-on-demand services deployed on smartphone.

The Importance of Peers for Visually Impaired Users of Social Media

For many social media is playing an important role in everyday life. People with visual impairments (VIs) are also using social media quite extensively. In this paper we analyze factors that might explain why social media is used, and the role of social media on better quality of life. We use a structural equation modeling technique to identify factors that are associated with a better life.

Talk, silence and the study of situated action

This theoretical paper discusses some conceptual and epistemological issues in the study of situated action in CSCL environments, especially focusing on ethnomethodology (EM) and conversation analysis (CA). It is argued that EM and CA approaches provide relevant and fruitful research strategies and methodologies. Some theoretical and epistemological peculiarities, weaknesses and biases of the approaches are identified and discussed. Finally, activity theory is briefly discussed as a potential alternative methodological and theoretical approach.

A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

Increased user motivation from interaction process leads to improved interaction, resulting in increased motivation again, which forms a positive self-propagating cycle. Therefore, a system will be more effective if the user is more motivated. Especially for students with dyslexia, it is common for them to experience more learning difficulties that affect their learning motivation. Thats why we need to employ techniques to enhance user motivation in the interaction process. In this research, we will present a system architecture for motivation-enhanced learning and the detailed process of the construction of our motivation model using ontological approach for students with dyslexia. The proposed framework of the personalised learning system incorporates our motivation model and corresponding personalisation mechanism aiming to improve learning motivation and performance of students with dyslexia. Additionally, we also provide examples of inference rules and a use scenario for illustration of personalisation to be employed in our system.

Combining Information from Web Based Survey and Telephone Survey

An on-line survey on the Internet, a web survey, is a new way of collecting data from the users of a web site. However, if answering is voluntary, a low response rate can be expected, and an analysis based on the observed data alone may give biased and misleading results due to self selection. The research question we address in this paper is; what solutions can be recommended to correct for the possible bias due to non-response? Our suggestion is to perform an additional survey, with better quality with respect to the response rate and self selection bias, to combine the two surveys by statistical modelling. This approach is used to study a specific web site, where data were collected both by web and telephone surveys.

A Novel Approach for the Population and Adaptation of Ontology-Based User Profiles

User personalisation within context-aware applications has become increasingly prevalent in recent years. The use of ontologies enriched with semantics has enabled the creation of highly relevant user profiles, which have been used to facilitate the personalisation of assistive services. Existing work highlights the challenge of aiding non-expert users to intuitively manage their own profiles. This paper details a new approach to help enable the automatic creation, population and adaptation of ontology-based user profiles. Primarily, the research aims to create and enrich ontological profiles over time, for the purposes of user personalisation. The approach has been realised through the development of an intuitive desktop application, which has been evaluated through a series of experiments.