2015
Plessas, Athanasios; Georgiadou, Olga; Stefanis, Vassilios; Komninos, Andreas; Garofalakis, John
Assessing Physical Location as a Potential Contextual Cue For Adaptive Mobile Contact Lists Proceedings Article
In: Ubiquitous Computing and Communications (IUCC), 2015 IEEE International Conference on, pp. 1316-1324, IEEE, 2015, ISBN: 978-1-5090-0153-8.
Abstract | Links | BibTeX | Ετικέτες: adaptive contact list, call prediction, context awareness, physical location
@inproceedings{Plessas2015,
title = {Assessing Physical Location as a Potential Contextual Cue For Adaptive Mobile Contact Lists},
author = {Athanasios Plessas and Olga Georgiadou and Vassilios Stefanis and Andreas Komninos and John Garofalakis},
url = {http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.195},
doi = {10.1109/CIT/IUCC/DASC/PICOM.2015.195},
isbn = {978-1-5090-0153-8},
year = {2015},
date = {2015-10-01},
booktitle = {Ubiquitous Computing and Communications (IUCC), 2015 IEEE International Conference on},
pages = {1316-1324},
publisher = {IEEE},
abstract = {The retrieval of the appropriate contact in order to start a new communication session from the contact repository of mobile devices can be a time consuming procedure since mobile contact lists usually contain hundreds of items. Several researchers have focused in the past on predicting the next contact a user is likely to call, a task that could prove useful in designing adaptive context-aware interfaces for the mobile contact list. Most of the researchers propose several contextual dimensions that could be used to predict the next callee, location being one of them. However, none of these research works have ever examined the impact of location on mobile communications and only few have actually incorporated this contextual dimension on their implementations. In this paper, we examine physical location as a contextual cue for adaptive mobile contact lists by analyzing call logs from the Nokia Mobile Data Challenge dataset. Our work indicates that, contrary to previous literature, the consideration of physical location as a context dimension does not necessarily lead to improvements in the accuracy of predicting the likelihood of communication with contacts for all types of users included in the dataset under review. Finally, we also discuss the possible reasons behind this limited impact.},
keywords = {adaptive contact list, call prediction, context awareness, physical location},
pubstate = {published},
tppubtype = {inproceedings}
}
The retrieval of the appropriate contact in order to start a new communication session from the contact repository of mobile devices can be a time consuming procedure since mobile contact lists usually contain hundreds of items. Several researchers have focused in the past on predicting the next contact a user is likely to call, a task that could prove useful in designing adaptive context-aware interfaces for the mobile contact list. Most of the researchers propose several contextual dimensions that could be used to predict the next callee, location being one of them. However, none of these research works have ever examined the impact of location on mobile communications and only few have actually incorporated this contextual dimension on their implementations. In this paper, we examine physical location as a contextual cue for adaptive mobile contact lists by analyzing call logs from the Nokia Mobile Data Challenge dataset. Our work indicates that, contrary to previous literature, the consideration of physical location as a context dimension does not necessarily lead to improvements in the accuracy of predicting the likelihood of communication with contacts for all types of users included in the dataset under review. Finally, we also discuss the possible reasons behind this limited impact.
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.
2013
Plessas, Athanasios; Stefanis, Vassilios; Komninos, Andreas; Garofalakis, John
Using Communication Frequency and Recency Context to Facilitate Mobile Contact List Retrieval Journal Article
In: International Journal of Handheld Computing Research (IJHCR), vol. 4, no. 4, pp. 52–71, 2013, ISSN: 1947-9158.
Abstract | Links | BibTeX | Ετικέτες: Call Log Analysis, call prediction, Mobile Contact List, Mobile Contact Retrieval, Mobile Context
@article{Plessas:2013:UCF:2604004.2604008,
title = {Using Communication Frequency and Recency Context to Facilitate Mobile Contact List Retrieval},
author = { Athanasios Plessas and Vassilios Stefanis and Andreas Komninos and John Garofalakis},
url = {http://dx.doi.org/10.4018/ijhcr.2013100104},
doi = {10.4018/ijhcr.2013100104},
issn = {1947-9158},
year = {2013},
date = {2013-01-01},
journal = {International Journal of Handheld Computing Research (IJHCR)},
volume = {4},
number = {4},
pages = {52--71},
publisher = {IGI Global},
address = {Hershey, PA, USA},
abstract = {As mobile contact lists get bigger and bigger the cognitive load on the user increases while trying to retrieve the next contact to start a communication session. In this paper we focus on the task of retrieving a contact when the purpose is to start a phone call, examining mobile users' call logs and showing that it is possible to accurately predict the next contact to be called using relatively simple heuristics and algorithms that describe usage context. The authors present and discuss the results of the proposed method applied on a dataset collected from an experiment the authors organised involving 25 mobile users.},
keywords = {Call Log Analysis, call prediction, Mobile Contact List, Mobile Contact Retrieval, Mobile Context},
pubstate = {published},
tppubtype = {article}
}
As mobile contact lists get bigger and bigger the cognitive load on the user increases while trying to retrieve the next contact to start a communication session. In this paper we focus on the task of retrieving a contact when the purpose is to start a phone call, examining mobile users' call logs and showing that it is possible to accurately predict the next contact to be called using relatively simple heuristics and algorithms that describe usage context. The authors present and discuss the results of the proposed method applied on a dataset collected from an experiment the authors organised involving 25 mobile users.