By their Apps you Shall Understand them: Mining Large-scale Patterns of Mobile Phone Usage
T. Do, and D. Gatica-Perez. Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM '10), Limassol, Cyprus, page 27:1-27:10. ACM, (2010)
DOI: 10.1145/1899475.1899502
Abstract
Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usage patterns exhibit not only what people are consistently interested in but also the way in which they use their phones, and can help improving phone design and personalized services. This work aims at mining automatically usage patterns from apps data recorded continuously with smartphones. A new probabilistic framework for mining usage patterns is proposed. Our methodology involves the design of a bag-of-apps model that robustly represents level of phone usage over specific times of the day, and the use of a probabilistic topic model that jointly discovers patterns of usage over multiple applications and describes users as mixtures of such patterns. Our framework is evaluated using 230 000+ hours of real-life app phone log data, demonstrates that relevant patterns of usage can be extracted, and is objectively validated on a user retrieval task with competitive performance.
%0 Conference Paper
%1 DoGaticaPerez10MUM
%A Do, Trinh-Minh-Tri
%A Gatica-Perez, Daniel
%B Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM '10), Limassol, Cyprus
%D 2010
%K v1205 springer paper embedded ai mobile application user interaction pattern recognition analysis test zzz.sfit
%P 27:1-27:10
%R 10.1145/1899475.1899502
%T By their Apps you Shall Understand them: Mining Large-scale Patterns of Mobile Phone Usage
%X Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usage patterns exhibit not only what people are consistently interested in but also the way in which they use their phones, and can help improving phone design and personalized services. This work aims at mining automatically usage patterns from apps data recorded continuously with smartphones. A new probabilistic framework for mining usage patterns is proposed. Our methodology involves the design of a bag-of-apps model that robustly represents level of phone usage over specific times of the day, and the use of a probabilistic topic model that jointly discovers patterns of usage over multiple applications and describes users as mixtures of such patterns. Our framework is evaluated using 230 000+ hours of real-life app phone log data, demonstrates that relevant patterns of usage can be extracted, and is objectively validated on a user retrieval task with competitive performance.
%@ 978-1-4503-0424-5
@inproceedings{DoGaticaPerez10MUM,
abstract = {Mobile phones are becoming more and more widely used nowadays, and people do not use the phone only for communication: there is a wide variety of phone applications allowing users to select those that fit their needs. Aggregated over time, application usage patterns exhibit not only what people are consistently interested in but also the way in which they use their phones, and can help improving phone design and personalized services. This work aims at mining automatically usage patterns from apps data recorded continuously with smartphones. A new probabilistic framework for mining usage patterns is proposed. Our methodology involves the design of a bag-of-apps model that robustly represents level of phone usage over specific times of the day, and the use of a probabilistic topic model that jointly discovers patterns of usage over multiple applications and describes users as mixtures of such patterns. Our framework is evaluated using 230 000+ hours of real-life app phone log data, demonstrates that relevant patterns of usage can be extracted, and is objectively validated on a user retrieval task with competitive performance.},
added-at = {2012-11-30T16:43:49.000+0100},
author = {Do, Trinh-Minh-Tri and Gatica-Perez, Daniel},
biburl = {https://www.bibsonomy.org/bibtex/22c36b03cc7c17905aed3c8c51b4dc601/flint63},
booktitle = {Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM '10), Limassol, Cyprus},
doi = {10.1145/1899475.1899502},
file = {ACM Digital Library:2010/DoGaticaPerez10MUM.pdf:PDF},
groups = {public},
interhash = {d9e354981f3614521f9aaf82c46ff0af},
intrahash = {2c36b03cc7c17905aed3c8c51b4dc601},
isbn = {978-1-4503-0424-5},
keywords = {v1205 springer paper embedded ai mobile application user interaction pattern recognition analysis test zzz.sfit},
organization = {ACM},
pages = {27:1-27:10},
timestamp = {2018-04-16T11:59:21.000+0200},
title = {By their Apps you Shall Understand them: Mining Large-scale Patterns of Mobile Phone Usage},
username = {flint63},
year = 2010
}