Interaction-driven User Interface Personalisation for Mobile News Systems Marios Constantinides A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy of University College London. Department of Computer Science University College London July 2018 ii iii I, Marios Constantinides, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the work. “Family is where life begins and love never ends”— Anonymous To my family Abstract User interfaces of mobile apps offer personalised experience primarily through manual customisation rather than spontaneous adaptation. This thesis investigates methods for adaptive user interfaces in the context of future mobile news apps that are expected to systematically monitor users’ news access patterns and adapt their interface and interaction in response. Although mobile news services are now able to recommend news that a user would be likely to read, there has not been equiva- lent progress in personalising the way that news content is accessed and read. This thesis addresses key issues for the development of adaptive user interfaces in the mobile environment and contributes to the existing literature of adaptive user inter- faces, user modelling, and personalisation in the domain of news in four ways. First, using survey methods it explores differences in how people consume and read news content on mobile news apps and it defines a News Reader Typology that charac- terises the individual news consumer. Second, it develops a method for monitoring news reading patterns through a deployed news app, namely Habito News, and it proposes a framework for modelling users by analysing those patterns; machine learning algorithms are exploited selectively in the analysis. Third, it explores the design space of personalised user interfaces and interactions that would be tailored to the needs and preferences of individual news readers. Finally, it demonstrates the effectiveness of automatic adaptation through Habito News, the prototype mobile news app that was developed, which systematically monitors users’ news reading interaction behaviour and automatically adjusts its interface in response to their news reading characteristics. The results indicate the feasibility of user interface personalisation and help shape the future of automatically changing user interfaces by systematic monitoring, profiling and adapting the interface and interaction. Impact Statement In user interface research, a widely used paradigm is the “one-size-fits-all” in which all users of a system and across systems interact with the same user interface to op- timise consistency and predictability. The inability, however, of this paradigm to address the particular needs of different users and the specific demands of differ- ent tasks, led to adaptation or personalisation of the user interfaces. Adaptation in user interfaces has been a longstanding interest with many successful applications demonstrating its effectiveness and desirability from users. Traditionally, the two paradigms of applying adaptation are the adaptable and the adaptive. Adaptable sys- tems allow users to manually manipulate and configure their user interfaces based on their preferences. On the other hand, adaptive systems rely entirely on the sys- tem to build a user profile of the current user and exploit that profile to personalise the user interface without any explicit input from the user. This thesis investigates the use of adaptive user interfaces and explores its effectiveness in mobile news reading. The domain of news is a challenging and relevant field for developing and investigating personalisation in user interfaces. Reading the news is a highly indi- vidual experience with marked differences in the ways people read and access the news. Although many news systems are able to help users find the right news by recommending stories they might want to read, their user interfaces do not respond to their habits, preferences and the particular ways of consuming the news content. This thesis delivers a concept demonstrator of an adaptive news application, coined Habito News, that learns users’ interaction behaviour and adjusts its displays in response to those interaction patterns. viii Impact Statement The thesis contributes in the research areas of User Modelling, Personalisa- tion and the wider HCI. It suggests a News Reader typology that describes different kinds of news reading behaviour relating to news consumption patterns and habits. Furthermore, it proposes a framework that characterises the hierarchical relation- ship of abstracted factors relating to news reading behaviour with data that can be captured from monitoring users’ news reading interaction behaviour. It explores different user modelling techniques and implements models that are capable of de- tecting the different kinds of news reading behaviour. The inferred models from users’ interaction behaviour serve as the basis of adapting and reconfiguring the user interface to suit the individual ways of consuming news content. Different user interfaces are examined and proposed for different kinds of news reading behaviour. In addition to its contribution to the academic research, this thesis has an im- pact in commercial news services and providers. The outcomes of this research can be incorporated in existing news services to model their users’ interaction behaviour that would enable their service to provide a more personalised news experience, tai- lored to the individual user characteristics. The proposed News Reader typology can serve as a starting point in classifying different news reader types not only by the type of content they consume but also by the ways that content is consumed. Acknowledgements While writing these acknowledgements, I kept reminding myself that “to lakwnizein´ esti´ filosofein´ ”, a motto originated from Ancient Greeks and refers to a person’s ability to express briefly and comprehensively. I hope that I will be able to express my gratitude to all those who I am indebted to for the completion of this thesis in such a brief manner. The accomplishment of this thesis has been one of my greatest challenges in life and it would not be possible without the support of various people. First and foremost, I would like to express my sincere gratitude to my Ph.D. advisor, Dr. John Dowell, who thoughtfully guided me through this thesis with his valuable advice and comments. I am grateful for the opportunity to work in a research area that was new for me, and the confidence that he has shown for my work and ideas. Next, I would like to thank Dr. Sylvain Malacria, who played a vital role in the early stages of my research work with his valuable feedback and immediate help whenever seeked. I am also grateful to Dr. Paul Marshall, who gave me the opportunity to be involved in a side project at UCLIC, co-supervising Yuval’s Cohen MSc. dissertation. I want to also thank the two students that I worked with during their dissertations, David Johnson and Danyaal Masood, and all the members of the UCLIC group. I am also indebted to the research group DMAC in University of Cyprus and particularly to Dr. Marios Belk, Dr. Panagiotis Germanakos and Prof. George Samaras for their continuous help and support throughout my research. While at UCL, I have met many brilliant Ph.D. students and postdocs. I want to thank them all for crossing paths, exchanging and sharing interesting discus- sions and ideas about my work. Specifically, I want to thank Alex Eftychiou, Hugo x Acknowledgements Lopez-Tovar, Frederik Brudy, Marta Ceccinatto, George Spithourakis, Manal Ad- ham, Ivan Sanchez. Special thanks to Peter Hayes and Dr. Vasileios Lampos with whom I shared a lot over the last year of my Ph.D. During my Ph.D., I also had the opportunity to undertake an internship posi- tion at Telefonica Alpha in Barcelona. While there, I have met beautiful and curious minds that helped me improve my skills and provided useful and constructive com- ments that led me to the completion of this thesis. Among them, special thanks to Dr. Alexandar Matic, who gave me the opportunity to work with him and Pascal Weinberger, a young curious mind that helped me to improve the way I approach problems and solving them. I cannot forget my Greek and Cypriot friends in London and back home, who supported me through all those years: Giorgos Tsioupis, Savvas Kouspou, Sav- vas Hadjiloukas, Costas Constantinou, Christos Tolakis, Emilia Kaprou, Constanti- nos Fasoulis, Silouanos Nicolaou, Andreas Hadjinicolaou, Lina Joseph and oth- ers. Further, my old and current flatmates in London, my brother Argyris Con- stantinides, Marios Mintzis, Xanthoula Christodoulou and Anna Stavrinidies, with whom I shared innumerable moments discussing my ideas and achievements dur- ing my Ph.D. Special thanks should be given to Popi Christopoulou, who always inspired me to reach my full potential. I also want to thank separately a close friend, Andreas Panteli, for his endless support and constant encouragement at times where I felt unmotivated and drained. Last but not least, I would like to thank my family. They say “family is where life begins and love never ends”. Nothing would have ever been possible without their unconditional support and appreciation. My mother, Louiza, and my father, Costas, who encouraged me to step outside my comfort zone and pursue my pas- sion. My brother Argyris, who was always there for me and to whom I want to express my deep gratitude for his countless support. My uncles, cousins and the rest of my family in Cyprus for their unconditional love. Finally, I am indebted to my grandparents, Argyris and Martha, for their valuable financial support through- out the course of my Ph.D., but more importantly their timeless love. List of Publications The text of the thesis is partly based upon the following publications: • M. Constantinides. ”Apps with habits: Adaptive interfaces for news apps. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp.