Predicting user preferences of environment design: a perceptual mechanism of user interface customisation

ABSTRACT It is a well-known fact that users vary in their preferences and needs. Therefore, it is very crucial to provide the customisation or personalisation for users in certain usage conditions that are more associated with their preferences. With the current limitation in adopting perceptual processing into user interface personalisation, we introduced the possibility of inferring interface design preferences from the user’s eye-movement behaviour. We firstly captured the user’s preferences of graphic design elements using an eye-tracker. Then we diagnosed these preferences towards the region of interests to build a prediction model for interface customisation. The prediction models from eye-movement behaviour showed a high potential for predicting users’ preferences of interface design based on the paralleled relation between their fixation and saccadic movement. This mechanism provides a novel way of user interface design customisation and opens the door for new research in the areas of human–computer interaction and decision-making.

[1]  Jinhyung Kim,et al.  User-centered innovative technology analysis and prediction application in mobile environment , 2013, Multimedia Tools and Applications.

[2]  Alexander Pollatsek,et al.  Using E-Z Reader to simulate eye movements in nonreading tasks: a unified framework for understanding the eye-mind link. , 2012, Psychological review.

[3]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[4]  Nitesh V. Chawla,et al.  Engagement vs performance: using electronic portfolios to predict first semester engineering student retention , 2014, LAK.

[5]  G. Hesslow The current status of the simulation theory of cognition , 2012, Brain Research.

[6]  Jin-Hyuk Hong,et al.  Understanding and prediction of mobile application usage for smart phones , 2012, UbiComp.

[7]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Nicola C. Anderson,et al.  Curious eyes: Individual differences in personality predict eye movement behavior in scene-viewing , 2012, Cognition.

[10]  Fredrik H. Karlsson,et al.  Gut metagenome in European women with normal, impaired and diabetic glucose control , 2013, Nature.

[11]  K. Stanovich,et al.  Cognitive Ability and Variation in Selection Task Performance , 1998 .

[12]  Elizabeth R Schotter,et al.  Task effects reveal cognitive flexibility responding to frequency and predictability: Evidence from eye movements in reading and proofreading , 2014, Cognition.

[13]  Samuel Kounev,et al.  Model-based self-adaptive resource allocation in virtualized environments , 2011, SEAMS '11.

[14]  Ryen W. White,et al.  Predicting user interests from contextual information , 2009, SIGIR.

[15]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[16]  J. Rauthmann,et al.  Eyes as windows to the soul: Gazing behavior is related to personality , 2012 .

[17]  Yi Li,et al.  A hybrid recommendation algorithm adapted in e-learning environments , 2012, World Wide Web.

[18]  Kurt Geihs,et al.  Achieving User Participation for Adaptive Applications , 2012, UCAmI.

[19]  T. Foulsham,et al.  Saliency and scan patterns in the inspection of real-world scenes: Eye movements during encoding and recognition , 2009 .

[20]  Ed H. Chi,et al.  Using information scent to model user information needs and actions and the Web , 2001, CHI.

[21]  Fokie Cnossen,et al.  Adaptive support for user interface customization: a study in radiology , 2015, Int. J. Hum. Comput. Stud..

[22]  Chenggang Bai,et al.  Graphical User Interface Reliability Prediction Based on Architecture and Event Handler Interaction , 2015 .

[23]  M. Bradley,et al.  Probing picture perception: activation and emotion. , 1996, Psychophysiology.

[24]  W. Fleeson,et al.  The End of the Person–Situation Debate: An Emerging Synthesis in the Answer to the Consistency Question , 2008 .

[25]  Stéphane Ducasse,et al.  Seamless composition and reuse of customizable user interfaces with Spec , 2014, Sci. Comput. Program..

[26]  Yong Se Kim,et al.  Learning Styles Diagnosis Based on User Interface Behaviors for the Customization of Learning Interfaces in an Intelligent Tutoring System , 2006, Intelligent Tutoring Systems.

[27]  M. McHugh Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[28]  Constantine Stephanidis,et al.  Encapsulating intelligent interactive behaviour in unified user interface artefacts , 2000, Interact. Comput..

[29]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[30]  Francis C. M. Lau,et al.  A Context-Aware Decision Engine for Content Adaptation , 2002, IEEE Pervasive Comput..

[31]  Geraldine Clarebout,et al.  The contribution of learner characteristics in the development of computer-based adaptive learning environments , 2011, Comput. Hum. Behav..

[32]  Jaime Arguello,et al.  Task complexity, vertical display and user interaction in aggregated search , 2012, SIGIR '12.

[33]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[34]  Miltiades E. Anagnostou,et al.  Context and Community Awareness in Support of User Intent Prediction , 2014, Context in Computing.

[35]  Chris D. Nugent,et al.  Ontological User Profile Modeling for Context-Aware Application Personalization , 2012, UCAmI.

[36]  Christophe Hurter,et al.  Visual scanning as a reference framework for interactive representation design , 2011, Inf. Vis..

[37]  Danilo Avola,et al.  Design of an efficient framework for fast prototyping of customized human-computer interfaces and virtual environments for rehabilitation , 2013, Comput. Methods Programs Biomed..

[38]  Albert A. Rizzo,et al.  Adapting user interfaces for gestural interaction with the flexible action and articulated skeleton toolkit , 2013, Comput. Graph..

[39]  Matthias Peissner,et al.  MyUI: generating accessible user interfaces from multimodal design patterns , 2012, EICS '12.

[40]  Torsten Suel,et al.  Modeling and predicting user behavior in sponsored search , 2009, KDD.

[41]  Weidong Huang Establishing aesthetics based on human graph reading behavior: two eye tracking studies , 2011, Personal and Ubiquitous Computing.