Name
Abstract | ||
|---|---|---|
This paper is prompted by the overall question 'what is the most effective way to recognise disruptive smartphone interruptions?'. We design our experiments to answer 3 questions: 'Do users revise what they perceive as disruptive incoming calls as time goes by?', 'How do different types of machine-learners (lazy, eager, evolutionary, ensemble) perform on this task?' and 'Can we restrict the initial amount of data and/or the number of features we need to make predictions without degrading performance?'. We consider these questions using Cambridge University's Device Analyzer dataset. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2638728.2641701 | UbiComp Adjunct |
Keywords | Field | DocType |
miscellaneous,interruptions,smartphones,notifications,learning | World Wide Web,Computer science,Human–computer interaction,restrict | Conference |
Citations | PageRank | References |
2 | 0.38 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremiah Smith | 1 | 2 | 0.38 |
| Anna Lavygina | 2 | 2 | 0.38 |
| Alessandra Russo | 3 | 1022 | 80.10 |
| Naranker Dulay | 4 | 1450 | 172.63 |