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

Technological framework for ubiquitous interactions using context–aware mobile devices

This report presents research and development of dedicated system architecture, designed to enable its users to interact with each other as well as to access information on Points of Interest that exist in their immediate environment. This is accomplished through managing personal preferences and contextual information in a distributed manner and in real-time. The advantage of this system architecture is that it uses mobile devices, heterogeneous sensors and a selection of user interface paradigms to produce a sociotechnical framework to enhance the perception of the environment and promote intuitive interactions. The thrust of the work has been on software development and component integration. Iterative prototyping was adopted as a development method in order to effectively implement the users’ feedback and establish a platform for collaboration that closely meets the requirements and aids their decision-making process. The requirement acquisition was followed by the system-modelling phase in order to produce a robust software prototype. The implementation includes component-based development and extensive use of design patterns over native programming. Conclusively, the software product has become the means to evaluate differences in the use of mixed reality technologies in a ubiquitous scenario. The prototype can query a number of context sources such as sensors, or details of the personal profile, to acquire relevant data. The data (and metadata) is stored in opensource structures, so that they are accessible at every layer of the system architecture and at any time. By proactively processing the acquired context, the system can assist the users in their tasks (e.g. navigation) without explicit input – e.g. by simply creating a gesture with the device. However, advanced interaction with the application via the user interface is available for requests that are more complex. Representations of the real world objects, their spatial relations and other captured features of interest are visualised on scalable interfaces, ranging from 2D to 3D models and from photorealism to stylised clues and symbols. Two principal modes of operation have been implemented; one, using geo-referenced virtual reality models of the environment, updated in real time, and second, using the overlay of descriptive annotations and graphics on the video images of the surroundings, captured by a video camera. The latter is referred to as augmented reality. The continuous feed of the device position and orientation data, from the GPS receiver and the digital compass, into the application, makes the framework fit for use in unknown environments and therefore suitable for ubiquitous operation. This is one of the novelties of the proposed framework, because it enables a whole range of social, peer-to-peer interactions to take place. The scenarios of how the system could be employed to pursue these remote interactions and collaborative efforts on mobile devices are addressed in the context of urban navigation. The conceptual design and implementation of the novel location and orientation based algorithm for mobile AR are presented in detail. The system is, however, multifaceted and capable of supporting peer-to-peer exchange of information in a pervasive fashion, usable in various contexts. The modalities of these interactions are explored and laid out in several scenarios, but particularly in the context of user adoption. Two evaluation tasks took place. The preliminary evaluation examined certain aspects that influence user interaction while being immersed in a virtual environment, whereas the second summative evaluation compared the utility and certain usability aspects of the AR and VR interfaces

A model for representing the motivational and cultural factors that influence mobile phone usage variety

Mobile phone usage involves the mobile phone, the telecommunications system, mobile phone users, and the adoption and use of the system. Mobile communications is a complex and rapidly changing industry consisting of the hardware, software, network and business aspects. Mobile phone users are influenced by demographic, social, cultural and contextual factors that complicate the understanding of mobile phone usage. Advances in technology and market competition drive the addition of new services and features. In contrast, human cognition and attention are more constrained and many users find it difficult to cope with the cognitive demands of mobile phone technology. The aim of this study is to develop a model for representing the influence of motivational needs and cultural factors on mobile phone usage variety. The link between motivational needs and mobile phone usage variety, the cultural factors that influence mobile phone usage variety, as well as usage spaces as an approach to representing usage variety, are researched. The research encompasses a literature study, structured interviews, a pilot study and a survey. The pilot study and survey yielded data about mobile phone usage of university students under the age of 30 in South Africa. The results from the statistical analysis were triangulated with the findings of the literature study and the observations made about mobile phone usage during this two-year period. A final survey was conducted to verify the model. The contribution of this study is a mobile phone technology usage model (MOPTUM) for representing the motivational and cultural factors that influence mobile phone usage variety in such a way that users can use the model to express their mobile phone usage needs in non-technical terms while marketers and designers can use the model to convert the expressed user needs into the features required. MOPTUM draws on concepts and models from sociology, computer-supported cooperative work, human-computer interaction and technology adoption models from the field of marketing. MOPTUM verifies some existing findings on mobile phone usage and then integrates and extends these existing models to provide a new model for understanding the motivational and cultural factors that influence mobile phone usage variety.ComputingPh. D. (Computer Science

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence