2014
Stefanis, Vassilios; Plessas, Athanasios; Komninos, Andreas; Garofalakis, John
Frequency and recency context for the management and retrieval of personal information on mobile devices Journal Article
In: Pervasive and Mobile Computing, vol. 15, pp. 100 - 112, 2014, ISSN: 1574-1192, (Special Issue on Information Management in Mobile ApplicationsSpecial Issue on Data Mining in Pervasive Environments).
Abstract | Links | BibTeX | Ετικέτες: call prediction, Context, mobile personal information management
@article{Stefanis2014100,
title = {Frequency and recency context for the management and retrieval of personal information on mobile devices},
author = {Vassilios Stefanis and Athanasios Plessas and Andreas Komninos and John Garofalakis},
url = {http://www.sciencedirect.com/science/article/pii/S1574119213001119},
doi = {10.1016/j.pmcj.2013.08.002},
issn = {1574-1192},
year = {2014},
date = {2014-12-01},
journal = {Pervasive and Mobile Computing},
volume = {15},
pages = {100 - 112},
abstract = {Abstract As users store increasingly larger amounts of personal information on their mobiles, the task of retrieving such items (e.g., contacts) becomes more difficult. We show that users can be categorized by their communication patterns and that each category benefits differently from supporting contact management applications. By examining mobile user call logs, we show that it is possible to aid retrieval tasks using relatively simple heuristics and algorithms that describe usage context, using solely the dimensions of contact use frequency and recency. We compare and discuss the results of the proposed method applied on two different mobile datasets: a large dataset from NOKIA and a smaller dataset collected by ourselves.},
note = {Special Issue on Information Management in Mobile ApplicationsSpecial Issue on Data Mining in Pervasive Environments},
keywords = {call prediction, Context, mobile personal information management},
pubstate = {published},
tppubtype = {article}
}
Abstract As users store increasingly larger amounts of personal information on their mobiles, the task of retrieving such items (e.g., contacts) becomes more difficult. We show that users can be categorized by their communication patterns and that each category benefits differently from supporting contact management applications. By examining mobile user call logs, we show that it is possible to aid retrieval tasks using relatively simple heuristics and algorithms that describe usage context, using solely the dimensions of contact use frequency and recency. We compare and discuss the results of the proposed method applied on two different mobile datasets: a large dataset from NOKIA and a smaller dataset collected by ourselves.