The thesis comprises eight Chapters and is structured as follows:
Chapter 2 contextualises the research work and covers a literature review of the different research areas that the thesis draws and builds upon. It reviews research works within the areas of adaptive and adaptable user interfaces and user modelling that span across different application domains, including web and mobile, but also with a particular focus on the news domain.
Chapter 3 examines people’s mobile news consumption patterns and introduces a News Reader Typology that reflects the different ways mobile news readers con- sume and access news content.
Chapter 4 presents the design and implementation of an adaptive news research platform that facilitates the exploration of adaptivity in mobile news apps.
Chapter 5 explores the user model acquisition. It proposes a hierarchical framework for analysing news reading interaction patterns and reports two ap- proaches for modelling user interaction habits. It presents models that are capa- ble of predicting users’ news reader types and models that are capable of learning the individual reading characteristics that discriminate news reader types in order to construct an individual user profile. Rule-based models and machine learning algorithms were implemented.
Chapter 6 investigates the design space of adaptive user interfaces for news apps and provides empirical evidence through evaluation studies. It presents two controlled laboratory studies in which several designs were being tested and eval- uated that suit the different kinds of news reading behaviour. It also describes the generation of adaptation rules that will be used during the adaptation process.
Chapter 7 presents the final evaluation study of the adaptive news research platform in which the effectiveness of adaptation is examined in a field deployment study with Habito News.
Chapter 8 provides an elaborated discussion of the findings of this research, reflects on the lessons learned over the course of the research and discusses future directions of this thesis.
Chapter 2
Background and Related Work
This Chapter surveys research works within the areas of adaptive and adaptable user interfaces, mobile context-aware systems and user modelling techniques. The review begins with an introduction to personalised systems in relation to adaptive and adaptable user interfaces and discusses the main challenges in developing such systems. It continues by identifying gaps in prior works that have motivated this research with a particular focus in the domain of news in the mobile environment. It then highlights the importance of user modelling and presents different approaches that have been used in modelling users’ behaviour, interaction, and actions with user interfaces. The Chapter concludes with a discussion of the different lines of research works presented.
2.1
Adaptation and Personalisation
Adaptation and personalisation are commonly used terms in Human-Computer In- teraction (HCI) literature, which are used to describe software systems that adjust or alter their behaviour or user interface to an individual or a group of users by lever- aging information known about their users, context, platform and/or tasks (Gajos et al., 2010, 2006; Findlater et al., 2008; McGrenere et al., 2002; Kuflik et al., 2012). Although the terms may appear identical to the end user, in terms of the outcome, their practical usage and application is yet under controversy among the research community. In fact, researchers frequently use the terms interchangeably, however, there is a subtle difference between adaptation and personalisation. The following
Figure 2.1: Spectrum of adaptation in computer systems (Oppermann and Rasher, 1997).
section examines their differences through an in-depth analysis and investigation of personalised systems proposed across different domains.
In computing, two kinds of computer software have been developed to support users with their tasks and enhance their experience interacting with computers; the adaptable and the adaptive. According to Oppermann (1994) terminology, the term adaptable is used for a system in which alterations or adjustments to its behaviour or user interface are explicitly performed by the user. The adaptive term is mainly used for systems in which personalisation of the system’s behaviour or its user interface entirely relies on the system without any user intervention. Similarly, Weld et al. (2003) used the term customisation to describe an adaptable system in which per- sonalisation is directly requested by the user and the term adaptation to describe an adaptive system in which personalisation is automatically performed by the user in- terface without explicit user directives. Building on previous definitions, Sears and Jacko (2009) have expressed adaptable systems as computer software that make changes to the content or the user interface only as a result of the explicit interven- tion of the user, whereas adaptive systems dynamically organise their contents to meet the perceived needs of the user without any direct user intervention. Certainly, apart from the two extremes, hybrid solutions have been proposed that combine principles of the two. Figure 2.1 depicts the whole spectrum of adaptation in com- puter systems.
Adaptable systems have been widely deployed. Most commercials systems al- low users to manually change system parameters, provide individual interests and tailor the user interface to fit their needs or demands to complete specific tasks. Productivity software is a great example which illustrates the idea of manual adap-
2.1. Adaptation and Personalisation 13 tation. For example, Microsoft Word allows users to tailor toolbar items based on their preferences and tasks. McGrenere et al. (2002) showed that manual adaptation of the user interface found to be more appealing to users than automatic adapta- tion. Furthermore, web portals provide options through settings menus to specify the sorts of information users want to see. Mobile apps are now embedded with personalised mechanisms to provide the best possible experience to their users. Al- though adaptable systems are abundant in our everyday activity with computers and mobile devices, several issues arise about their feasibility and acceptability to users. The lack of the application domain, the additional effort and time to learn how to customise and use such systems effectively are barriers that prevent users from manually customising interfaces (Mackay, 1991). Even more, these kind of systems do not leverage the machine’s capabilities to determine and learn about its users, instead they rely on user-initiated adaptation, which might be considered not the optimal form of adaptation.
Contrarily, adaptive systems are still quite rare (Findlater and McGrenere, 2010, 2004; Oppermann, 1994). However, the explosive growth in size and use of the World Wide Web and the advent of smartphones increased the demand for more adaptive services and interfaces. News web portals, online shopping and banking systems are example services that incorporate personalisation features in their sys- tems. For example, Google News1generates recommendations using a collabora- tive filtering approach in which the learning process of producing recommendations is based on unobtrusively collected user and community data. Mobile news apps provide personalised content by pushing filtered articles predicted to match users’ interests such as Flipboard, News360, BuzzFeed, Feedly, Pulse or research proto- types such as WebClipping2 (Carreira et al., 2004), Buzzer (Phelan et al., 2009), SmartMedia (Gulla et al., 2014), LumiNews (Kazai et al., 2016), PEN (Garcin and Faltings, 2013), Focal (Garcin et al., 2014). Further, mobile advertisements are now being targeted to specific users based on information about their context, so- cial activity or other factors. In addition to the technological advancements, the
target audience of those adaptive services is, often, the Millennials (also known as Generation Y).This younger generation, therefore, use technology differently than previous generations (Deal et al., 2010) and might be more receptive to systems that adapt or change functionality themselves without any intervention. For exam- ple, the Elastic News project (BBC RD, 2014) is an example of such a service that delivers news in a ‘snackable’ format and targets young people; a hard to reach and engage audience in news.
Apart from the two extremes, hybrid solutions in which the user’s selects the adaptation from system suggested features. A prominent example of such system is the Amazon.com 2platform, which acquires information about its users implic- itly by monitoring previous purchases or the user’s click-stream and explicitly by receiving the user’s feedback and item ratings. For example, the different items that are being recommended at the top of the search result section might differ from one user to another.