Selecting Gestural User Interaction Patterns for Recommender Applications on Smartphones

Selecting Gestural User Interaction Patterns for Recommender Applications on Smartphones Wolfgang Worndl Jan Weicker Beatrice Lamche From TU München Presented by Jeremy Chan

Outline • Introduction – Algorithm v.s. User experience • Gestural user interaction patterns • Literature review • Experiment and survey setup • Objective • Considerations of mapping gestures • Test application setup • User study setup and methodology Presented by Jeremy Chan

Outline • Result analysis • Log file analysis • Survey results • Conclusion and future work Presented by Jeremy Chan

Algorithm v.s. User experience Presented by Jeremy Chan

Algorithm v.s. User experience (Cont’d) Presented by Jeremy Chan

Gestural user interaction patterns • 2 major categories • Touchscreen • Free-form • Single/ Double tap • Pinch/ Spread • One-finger-hold pinch • Fling/ Flick • Rectangular pattern • Shake/ Tilt device Presented by Jeremy Chan

Gestural user interaction patterns (Cont’d) Note: Spread Pinch One-finger-hold pinch Fling Flick/ Swipe Rectangular pattern Shake device Tilt device Presented by Jeremy Chan

Literature review • Gestural interactions perform better • Cho et al. propose a photo browsing system for mobile devices. They compared three types of interaction: a tilt-based interaction technique, an iPod wheel and a button-based browser to browse and search photos efficiently. The results show that the tilting technique is comparable to the controllability of buttons, more interesting than the other techniques and performed better than the iPod wheel. Presented by Jeremy Chan

Literature review (Cont’d) • Gestural interactions provide efficiency for users • Negulescu et al. examined the cognitive demands of motion gestures, taps and surface gestures. They show that these three techniques do not differ in reaction time. Moreover they found out that motion gestures result in much less time spent looking at the smartphone during walking than does tapping on the screen. Therefore motion gestures are advantageous in certain scenarios. Presented by Jeremy Chan

Literature review (Cont’d) • Willingness of using gestural interactions differs • Rico and Brewster applied a different focus on motion gestures for mobile devices. They found out that location and audience have a significant influence on a user’s willingness to interact with a mobile device by using motion gestures. Presented by Jeremy Chan

Experiment and survey setup – Objective • Develop a test application, simulating a typical mobile recommender system • Provide different input methods for functions typically found in recommender systems • Test which interaction patterns the user would choose in the successive study Presented by Jeremy Chan

Experiment and survey setup – Considerations of mapping gestures • Common purposes • Single tap • interaction with on-screen objects • Slide/ Fling • scrolling screen or dragging objects • Pinch/ Spread • zooming Presented by Jeremy Chan

Experiment and survey setup – Considerations of mapping gestures (Cont’d) • No restrictions • Free-form gestures • Useful in application-wide functions • Can be called any time regardless of the current screen of the application • “Home” button on most mobile systems Presented by Jeremy Chan

Experiment and survey setup – Test application setup • Basic setup • Scenario for the prototype is a movie search and recommendation application that resembles the Internet Movie Database (IMDb) mobile application Options Menu Main Menu Start Screen Search Interface Item List Item Details Screen Presented by Jeremy Chan

Experiment and survey setup – Test application setup (Cont’d) • Functions • All the following functions are available and implemented by at least 2 input options each * Options menu button is also available # Only Options menu button is available Presented by Jeremy Chan

Experiment and survey setup – Test application setup (Cont’d) List of recommendations (left) Item details with options menu (right) Presented by Jeremy Chan

Experiment and survey setup – User study setup and methodology • Specific target • Find out what input method for a given function is preferred by the test users • Procedures for each user • Practice navigating different functions and input methods for about 10 minutes • Perform a set of 18 instructions in the application in a certain order • The list mentions the required tasks only; the input method to perform them is not specified • Test which input method the test persons find more intuitive to use for a certain task Presented by Jeremy Chan

Experiment and survey setup – User study setup and methodology (Cont’d) • Beginning of the sequence of instructions • Find Random Item • Find Similar Item • Rate Item • Open Main Menu • Open My Recommendations • Exclude Item from Recommendations • … • Some of the requested functions appear several times in the list • Test whether participants would change their preferred input method for a particular function Presented by Jeremy Chan

Experiment and survey setup – User study setup and methodology (Cont’d) • Every user action is recorded in a log file • Fill out a survey concerning his or her opinions about • The input methods for the requested instructions • The handling of the gestures in particular • 16 persons with mixed backgrounds participated in the study • Majority completed all the given scenarios • Only a few users skipped a few tasks from the instruction list Presented by Jeremy Chan

Result analysis – Log file analysis * All users chose to use on-screen bookmark button under traditional controls (options menu button was never used) Presented by Jeremy Chan

Result analysis – Log file analysis (Cont’d) Presented by Jeremy Chan

Result analysis – Survey results • 1st part of survey • Ask the participants how intuitive they find the input methods for the 6 functions • On a scale from 1 to 5 • Higher number means “more intuitive” Presented by Jeremy Chan

Result analysis – Survey results (Cont’d) • Ask whether inclusion of an on-screen button is worth the necessary screen space for the function Presented by Jeremy Chan

Result analysis – Survey results (Cont’d) • 2nd part of survey • Determine which input method the user is in favorite of Presented by Jeremy Chan

Result analysis – Survey results (Cont’d) • Ask whether prior experience with touchscreen devices would have any relations to the previous findings • Ask about the ease of handling the 4 gestures • Participants considered all gestures except one-finger-hold pinch (OFHP) • Holding one finger on screen while adjusting the rating is difficult Presented by Jeremy Chan

Result analysis – Survey results (Cont’d) Presented by Jeremy Chan

Conclusion and future work • Major findings • Users prefer simpler, easier to handle gestures over the more complex ones • OFHP must be carefully calibrated for ease of handling • In most of the cases, a simple button is a favorite option for users • Unless content space is rare, do not omit buttons Presented by Jeremy Chan

Conclusion and future work (Cont’d) • Options menu was not very popular in any of the used cases • Extra effort • One gesture is well adopted in a particular function, but not in another • Double tap is widely accepted for bookmarking, but not for saving search parameters • Users may be afraid of accidentally tapping on other interface elements Presented by Jeremy Chan

Conclusion and future work (Cont’d) • Users would stay on one method/ input mode they prefer • They seldom switch between modes • Users with more experience with touchscreen devices are more open towards gestures than users with less experience Presented by Jeremy Chan

Conclusion and future work (Cont’d) • Future work • How more complex gestures can be introduced in mobile recommender systems? • Are there any changes in user acceptance if users get more and more used to complex motion gestures? • Quantifiable measurements between mobile user behavior, user acceptance of gestures and the accuracy of recommender results? Presented by Jeremy Chan

Selecting Gestural User Interaction Patterns for Recommender Applications on Smartphones