Mitzlaff, F.; Doerfel, S.; Hotho, A.; Jäschke, R. & Mueller, J.
(2014):
Summary of the 15th Discovery Challenge: Recommending Given Names.
In: 15th Discovery Challenge of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013, Prague, Czech Republic - Sctober 27, 2013. Proceedings,
Aachen, Germany.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
The 15th ECML PKDD Discovery Challenge centered around the recommendation
f given names. Participants of the challenge implemented algorithms
hat were tested both offline - on data collected by the name search
ngine Nameling - and online within Nameling. Here, we describe both
asks in detail and discuss the publicly available datasets. We motivate
nd explain the chosen evaluation of the challenge, and we summarize
he different approaches applied to the name recommendation tasks.
inally, we present the rankings and winners of the offline and the
nline phase.
@inproceedings{mitzlaff2014summary,
author = {Mitzlaff, Folke and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Mueller, Juergen},
title = {Summary of the 15th Discovery Challenge: Recommending Given Names},
booktitle = {15th Discovery Challenge of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013, Prague, Czech Republic - Sctober 27, 2013. Proceedings},
publisher = {CEUR-WS},
address = {Aachen, Germany},
year = {2014},
volume = {1120},
pages = {7--24},
url = {http://ceur-ws.org/Vol-1120/},
keywords = {RecSys, workshop, nameling, summary, 2014, ECMLPKDD, myown, KDE, inproceedings},
abstract = {The 15th ECML PKDD Discovery Challenge centered around the recommendation
f given names. Participants of the challenge implemented algorithms
hat were tested both offline - on data collected by the name search
ngine Nameling - and online within Nameling. Here, we describe both
asks in detail and discuss the publicly available datasets. We motivate
nd explain the chosen evaluation of the challenge, and we summarize
he different approaches applied to the name recommendation tasks.
inally, we present the rankings and winners of the offline and the
nline phase.}
}
%0 = inproceedings
%A = Mitzlaff, Folke and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Mueller, Juergen
%B = 15th Discovery Challenge of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013, Prague, Czech Republic - Sctober 27, 2013. Proceedings
%C = Aachen, Germany
%D = 2014
%I = CEUR-WS
%T = Summary of the 15th Discovery Challenge: Recommending Given Names
%U = http://ceur-ws.org/Vol-1120/
accepted for publication:
Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G.
(2013):
Tag Recommendations for SensorFolkSonomies.
In: Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China - October 12-16, 2013. Proceedings,
[Kurzfassung] [BibTeX][Endnote]
With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.
@inproceedings{mueller2013recommendations,
author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
title = {Tag Recommendations for SensorFolkSonomies},
booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings},
publisher = {ACM},
year = {2013},
pages = {New York, NY, USA},
note = {accepted for publication},
keywords = {sitc, rsweb, everyaware, tag, l3s, iteg, myown, recommendation, RecSys, widenoise, itegpub, sensor, folksonomy, 2013},
abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.}
}
%0 = inproceedings
%A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd
%B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings
%D = 2013
%I = ACM
%T = Tag Recommendations for SensorFolkSonomies
Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G.
(2013):
Tag Recommendations for SensorFolkSonomies.
In: Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China - October 12-16, 2013. Proceedings,
Aachen, Germany.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.
@inproceedings{mueller2013recommendations,
author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
title = {Tag Recommendations for SensorFolkSonomies},
booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings},
publisher = {CEUR-WS},
address = {Aachen, Germany},
year = {2013},
volume = {1066},
url = {http://ceur-ws.org/Vol-1066/},
keywords = {RecSys, itegpub, EveryAware, l3s, 2013, myown, iteg, inproceedings},
abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.}
}
%0 = inproceedings
%A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd
%B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings
%C = Aachen, Germany
%D = 2013
%I = CEUR-WS
%T = Tag Recommendations for SensorFolkSonomies
%U = http://ceur-ws.org/Vol-1066/
accepted for publication:
Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G.
(2013):
Tag Recommendations for SensorFolkSonomies.
In: Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China - October 12-16, 2013. Proceedings,
[Kurzfassung] [BibTeX][Endnote]
With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.
@inproceedings{mueller2013recommendations,
author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
title = {Tag Recommendations for SensorFolkSonomies},
booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings},
publisher = {ACM},
year = {2013},
pages = {New York, NY, USA},
note = {accepted for publication},
keywords = {RecSys, widenoise, rsweb, sensor, tag, everyaware, folksonomy, 2013, myown, recommendation},
abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.}
}
%0 = inproceedings
%A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd
%B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings
%D = 2013
%I = ACM
%T = Tag Recommendations for SensorFolkSonomies