An Analytic Model for Time Efficient Personal Hierarchies

Download Publication File

Abstract

Hierarchy structures such as file systems are widespread interfaces for item retrieval and selection tasks. Some hierarchies can be modified by end-users, such as application launchers on smartphones or pictures in a file folder. These modifiable hierarchies cannot benefit from an optimization made beforehand as their content, unknown during the design process, is constantly evolving. We hence propose an analytic model which designers can integrate in their system to recommend a range of local structure modifications (e.g., creating new folders) to end-users. Proposing a range of modifications gives flexibility to end-users regarding their own meaningful grouping and labeling choices to follow a recommendation. A first experiment confirms that the recommendations built on our model can lead to modified hierarchies resulting in faster theoretical selection times. A second experiment confirms that the theoretical selection times fit empirical selection times in different hierarchy visual layouts: linear, radial, and grid.

Publisher Link

https://dl.acm.org/citation.cfm?id=3300598

Citation

William Delamare, Ali Neshati, Pourang Irani, and Xiangshi Ren. 2019. An Analytic Model for Time Efficient Personal Hierarchies. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). ACM, New York, NY, USA, Paper 368, 11 pages. DOI: https://doi.org/10.1145/3290605.3300598. Honourable Mention Award.

Bibtext Entry

@inproceedings{Delamare:2019:AMT:3290605.3300598,
author = {Delamare, William and Neshati, Ali and Irani, Pourang and Ren, Xiangshi},
title = {An Analytic Model for Time Efficient Personal Hierarchies},
booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
series = {CHI '19},
year = {2019},
isbn = {978-1-4503-5970-2},
location = {Glasgow, Scotland Uk},
pages = {368:1--368:11},
articleno = {368},
numpages = {11},
url = {http://doi.acm.org/10.1145/3290605.3300598},
doi = {10.1145/3290605.3300598},
acmid = {3300598},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {hierarchy, modifiable, predictive model, recommendation},
}