Exploring a Design Space of Graphical Adaptive Menus

Abstract

Graphical Adaptive Menus are Graphical User Interface menus whose predicted items of immediate use can be automatically rendered in a prediction window. Rendering this prediction window is a key question for adaptivity to enable the end-user to efficiently differentiate predicted items from normal ones and to consequently select appropriate items. Adaptivity for graphical menus has been investigated more for normal screens, such as desktops, than for small screens, such as smartphones, where real estate imposes severe rendering constraints. To address this question, this article defines and explores a design space where graphical adaptive menus are structured based on Bertin’s eight visual variables (i.e., position, size, shape, value, color, orientation, texture, and motion) and their combination by comparing their rendering for small screens with respect to normal screens. Based on this design space, previously introduced graphical adaptive menus are revisited in terms of four stability properties (i.e., spatial, physical, format, and temporal), and new menu designs are introduced and discussed for both normal and small screens. The resulting set of graphical adaptive menu has been subject to a preference analysis from which a particular design emerged: the cloud menu, where predicted items are arranged in an adaptive tag cloud. We investigate empirically the effect of the cloud menu on the item selection time and the error rate with respect to a static menu and an adaptive linear menu. This article then suggests a set of usability guidelines for designers and practitioners to design graphical adaptive menus in general and cloud menus in particular.

Evaluating user perception and emotion of microinteractions using a contradictory semantic scale

“…For example, Jakobsson and Myers pointed out that animation could lessen the perception of wait times and increase satisfaction 32 . However, microinteraction engages many variables, such as direction, speed, rhythm, and track simultaneously overlapping; the resulting emotional variables are more complex 33 . When unlocking the iPhone, all apps move horizontally.…”

Exploring the user’s preferences of different adaptation policies in adaptive menu design

“…Graphical adaptation can also be used in the form of highlighting, bolding, coloring, underlining, or changing the font size [15][16][17]. Users tend to prefer menus that maintain spatial stability and dislike menus with unusual shapes or color-changing elements [18]. While ephemeral adaptation, which employs a temporal form, was found to be faster when prediction accuracy was high, users tend to prefer other forms of adaptation [19].…”

Adapting User Interfaces with Model-based Reinforcement Learning

“…Menus have received extensive attention in HCI research because they are widely used, and adaptation has potential to improve usability [4,55]. It is known that unexpected changes in menus can introduce a temporary performance drop, increase cognitive load, and potentially lead to the rejection of the adaption/techniques [4,55]. No general solution has been proposed for autonomous adaptation that could not only move items to the top, but handle reorganisations more comprehensively.…”

“…where B(i l ) is the level of activation of item i at location l in the memory, T is the current time, T j,i l is the time of the j th selection of i l , and ρ is a decay parameter equal to 0.5. User interest (or prediction scheme [55]) is given by the frequency distribution of commands selected during the previous interaction session, containing N clicks. Additional statistical models of user interest and expertise [18,19,21,51], can be plugged into our architecture.…”

“…To demonstrate the applicability of our approach, we tackle a challenging and open question in the field of adaptive interfaces: adaptive menus. Menus have received extensive attention in HCI research because they are widely used, and adaptation has potential to improve usability [4,55]. It is known that unexpected changes in menus can introduce a temporary performance drop, increase cognitive load, and potentially lead to the rejection of the adaption/techniques [4,55].…”