| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Mitzlaff, F., Doerfel, S., Hotho, A., Jäschke, R. & Mueller, J. | Summary of the 15th Discovery Challenge: Recommending Given Names | 2014 | 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 | inproceedings | URL |
| 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. |
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BibTeX:
@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},
year = {2014},
volume = {1120},
pages = {7--24},
url = {http://ceur-ws.org/Vol-1120/}
}
|
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| Mueller, J., Doerfel, S., Becker, M., Hotho, A. & Stumme, G. | Tag Recommendations for SensorFolkSonomies | 2013 | Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings | 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. |
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BibTeX:
@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}
}
|
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| Mueller, J., Doerfel, S., Becker, M., Hotho, A. & Stumme, G. | Tag Recommendations for SensorFolkSonomies | 2013 | Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings | inproceedings | URL |
| 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. |
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BibTeX:
@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},
year = {2013},
volume = {1066},
url = {http://ceur-ws.org/Vol-1066/}
}
|
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| Mueller, J., Doerfel, S., Becker, M., Hotho, A. & Stumme, G. | Tag Recommendations for SensorFolkSonomies | 2013 | Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings | 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. |
|||||
BibTeX:
@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}
}
|
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