Name
Abstract | ||
|---|---|---|
A variety of applications (app) installed on smart phones do greatly enrich our lives, but make it more difficult to organize our screens and folders. Predicting apps that will be in use next can benefit users a lot. In this poster, we propose some light-weighted Bayesian methods to predict the next app based on the app usage history. The evaluation on Mobile Data Challenge (MDC) dataset gives very encouraging results. In addition, we suggest a natural way to integrate the app prediction features to the user interface. Users would find it convenient to access the predicted apps with simple touches. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2494091.2494146 | UbiComp (Adjunct Publication) |
Keywords | Field | DocType |
predicting apps,app prediction feature,next app,user interface,mobile data challenge,app usage history,encouraging result,smart phone,simple touch,light-weighted bayesian method | World Wide Web,Computer science,Mobile deep linking,Smart phone,User interface,Mobile broadband,Multimedia | Conference |
Citations | PageRank | References |
15 | 0.66 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xun Zou | 1 | 16 | 1.29 |
| Wangsheng Zhang | 2 | 205 | 8.85 |
| Shijian Li | 3 | 1155 | 69.34 |
| Gang Pan | 4 | 1501 | 123.57 |