Name
Abstract | ||
|---|---|---|
Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1--3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2632048.2632052 | UbiComp |
Keywords | Field | DocType |
context prediction,miscellaneous,mobile data mining,rule mining | Middleware,World Wide Web,Mobile data mining,User experience design,Computer science,Human–computer interaction,Rule mining,Phone,Ranging,Mobile device,Cloud computing | Conference |
Citations | PageRank | References |
53 | 1.37 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vijay Srinivasan | 1 | 56 | 2.06 |
| Saeed Moghaddam | 2 | 53 | 1.37 |
| Abhishek Mukherji | 3 | 79 | 6.27 |
| Kiran K. Rachuri | 4 | 570 | 28.03 |
| Chenren Xu | 5 | 513 | 36.00 |
| Emmanuel Munguia Tapia | 6 | 1419 | 126.46 |