Name
Abstract | ||
|---|---|---|
Open-source mobile notification datasets are a rarity in the research community. Due to the sensitive nature of mobile notifications it is difficult to find a dataset which captures their features in such a way that their inherently personal information is kept private. For this reason, the majority of research in the domain of Notification Management requires ad-hoc software to be developed for capturing the data necessary to test hypotheses, train algorithms and evaluate proposed systems. As an alternative, this paper discusses the process, advantages and limitations with harnessing a large-scale mobile usage dataset for deriving a synthetic mobile notification dataset used in testing and improving an intelligent Notification Management System (NMS). |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3099023.3099046 | UMAP (Adjunct Publication) |
Field | DocType | Citations |
World Wide Web,Computer science,Software,Personally identifiable information,Management system | Conference | 0 |
PageRank | References | Authors |
0.34 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kieran Fraser | 1 | 0 | 2.03 |
| Bilal Yousuf | 2 | 13 | 4.53 |
| Owen Conlan | 3 | 447 | 63.88 |