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dc.contributor.authorSahami Shirazi, Alirezade
dc.contributor.authorLe, Huy Vietde
dc.contributor.authorHenze, Nielsde
dc.contributor.authorSchmidt, Albrechtde
dc.contributor.editorBoll, Susanne
dc.contributor.editorMaaß, Susanne
dc.contributor.editorMalaka, Rainer
dc.date.accessioned2017-11-22T14:54:40Z
dc.date.available2017-11-22T14:54:40Z
dc.date.issued2013
dc.identifier.isbn978-3-486-77856-4
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/7492
dc.description.abstractCurrent smartphones have virtually unlimited space to store contact information. Users typically have dozens or even hundreds of contacts in their address book. The number of contacts can make it difficult to find particular contacts from the linear list provided by current phones. Grouping contacts ease the retrieval of particular contacts and also enables to share content with specific groups. Previous work, however, shows that users are not willing to manually categorize their contacts. In this paper we inves-tigate the automatic classification of contacts in phones contact lists, using the user s communication history. Potential contact groups were determined in an online survey with 82 participants. We collect-ed the call and SMS communication history from 20 additional participants. Using the collected data we trained a machine-learning algorithm that correctly classified 59.2% of the contacts. In a pilot study in which we asked participants to review the results of the classifier we found that 73.6% of the re-viewed contacts were considered correctly classified. We provide directions to further improve the performance and argue that the current results already enable to ease the manual classification of mo-bile phone contacts.en
dc.language.isoenen
dc.publisherOldenbourg Verlag
dc.relation.ispartofMensch & Computer 2013: Interaktive Vielfalt
dc.subjectmobile phonede
dc.subjectmachine learningde
dc.subjectcontactde
dc.subjectfacetde
dc.subjectde
dc.titleAutomatic Classification of Mobile Phone s Contactsen
dc.typemuc: langbeitrag (vorträge)en
dc.pubPlaceMünchen
mci.document.qualitydigidoc
mci.reference.pages27-36de_DE
mci.conference.sessiontitleSession01: Kontextadaptive Systemede_DE


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