The personal information stored on the desktop usually reaches huge dimensions nowadays. Its handling is even more difficult, taking into account complex environments and tasks we work with. An efficient method of identifying the present working context would mean an easier management of the needed resources. In this paper we propose a new way of identifying desktop usage contexts, based upon a distance between documents, which also takes into account their access timestamps. We investigate and compare our technique with traditional term vector clustering, our initial experiments showing promising results with our proposed approach.
%0 Conference Paper
%1 ChiritaCostache+:PIM06
%A Chirita, Paul Alexandru
%A Costache, Stefania
%A Gaugaz, Julien
%A Nejdl, Wolfgang
%B SIGIR 2006 Workshop on Personal Information Management, Seattle WA, USA, 10-11.08.06
%D 2006
%K 2006 from:markusjunker l3s lang:en nepomuk
%T Desktop Context Detection Using Implicit Feedback
%U http://pim.ischool.washington.edu/pim06/files/chirita-paper.pdf
%X The personal information stored on the desktop usually reaches huge dimensions nowadays. Its handling is even more difficult, taking into account complex environments and tasks we work with. An efficient method of identifying the present working context would mean an easier management of the needed resources. In this paper we propose a new way of identifying desktop usage contexts, based upon a distance between documents, which also takes into account their access timestamps. We investigate and compare our technique with traditional term vector clustering, our initial experiments showing promising results with our proposed approach.
@inproceedings{ChiritaCostache+:PIM06,
abstract = {The personal information stored on the desktop usually reaches huge dimensions nowadays. Its handling is even more difficult, taking into account complex environments and tasks we work with. An efficient method of identifying the present working context would mean an easier management of the needed resources. In this paper we propose a new way of identifying desktop usage contexts, based upon a distance between documents, which also takes into account their access timestamps. We investigate and compare our technique with traditional term vector clustering, our initial experiments showing promising results with our proposed approach.
},
added-at = {2006-11-14T11:28:02.000+0100},
author = {Chirita, Paul Alexandru and Costache, Stefania and Gaugaz, Julien and Nejdl, Wolfgang},
biburl = {https://www.bibsonomy.org/bibtex/28bd73c19f0e2bcd1a14187eed8674c74/nepomuk},
booktitle = {SIGIR 2006 Workshop on Personal Information Management, Seattle WA, USA, 10-11.08.06},
interhash = {e58a86bc3ffa4bd999dad06ebdaac3cb},
intrahash = {8bd73c19f0e2bcd1a14187eed8674c74},
keywords = {2006 from:markusjunker l3s lang:en nepomuk},
timestamp = {2007-03-22T17:59:35.000+0100},
title = {Desktop Context Detection Using Implicit Feedback},
url = {http://pim.ischool.washington.edu/pim06/files/chirita-paper.pdf },
year = 2006
}