Name
Abstract | ||
|---|---|---|
As our business, academic, and personal lives continue to move at an ever-faster pace, finding times for busy people to meet has become an art. One of the most perplexing challenges facing groupware is effective asynchronous group scheduling (GS). This paper presents a lightweight interaction model for GS that can extend its reach beyond users of current group calendaring solutions. By expressing availability in terms of preferences, we create a flexible framework for GS that preserves plausible deniability while exerting social pressure to encourage honesty among users. We also propose an ontology that enables us to model user preferences with machine learning, predicting user responses to further lower cognitive load. The combination of visualization/direct manipulation with machine learning allows users to easily and efficiently optimize meeting times. We also suggest resulting design implications for this class of intelligent user interfaces. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1124772.1124929 | CHI |
Keywords | Field | DocType |
busy people,model user preference,effective asynchronous group scheduling,cognitive load,machine learning,design implication,lightweight interaction model,intelligent user interface,current group,user response,supervised learning | Ontology,Asynchronous communication,Pace,Collaborative software,Visualization,Computer science,Supervised learning,Human–computer interaction,Cognitive load,User interface,Multimedia | Conference |
ISBN | Citations | PageRank |
1-59593-372-7 | 10 | 0.73 |
References | Authors | |
19 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mike Brzozowski | 1 | 83 | 3.20 |
| Kendra Carattini | 2 | 10 | 0.73 |
| Scott Klemmer | 3 | 2977 | 197.02 |
| Patrick Mihelich | 4 | 85 | 6.57 |
| Jiang Hu | 5 | 10 | 0.73 |
| Andrew Y. Ng | 6 | 26065 | 1987.54 |