@inproceedings{mueller2013recommendations,
  abstract = {With the rising popularity of smart mobile devices, sensor data-based
 applications have become more and more popular. Their users record
 data during their daily routine or specifically for certain events.
 The application WideNoise Plus allows users to record sound samples
 and to annotate them with perceptions and tags. The app is being
 used to document and map the soundscape all over the world. The procedure
 of recording, including the assignment of tags, has to be as easy-to-use
 as possible. We therefore discuss the application of tag recommender
 algorithms in this particular scenario. We show, that this task is
 fundamentally different from the well-known tag recommendation problem
 in folksonomies as users do no longer tag fix resources but rather
 sensory data and impressions. The scenario requires efficient recommender
 algorithms that are able to run on the mobile device, since Internet
 connectivity cannot be assumed to be available. Therefore, we evaluate
 the performance of several tag recommendation algorithms and discuss
 their applicability in the mobile sensing use-case.},
  author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
  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},
  interhash = {23d1cf49208d9a0c8b883dc69d4e444d},
  intrahash = {6190d6064dfdb3b8d71f2898539e993e},
  note = {accepted for publication},
  pages = {New York, NY, USA},
  publisher = {ACM},
  title = {Tag Recommendations for SensorFolkSonomies},
  year = 2013
}

@misc{mitzlaff2013recommending,
  abstract = {All over the world, future parents are facing the task of finding a suitable
given name for their child. This choice is influenced by different factors,
such as the social context, language, cultural background and especially
personal taste. Although this task is omnipresent, little research has been
conducted on the analysis and application of interrelations among given names
from a data mining perspective.
  The present work tackles the problem of recommending given names, by firstly
mining for inter-name relatedness in data from the Social Web. Based on these
results, the name search engine "Nameling" was built, which attracted more than
35,000 users within less than six months, underpinning the relevance of the
underlying recommendation task. The accruing usage data is then used for
evaluating different state-of-the-art recommendation systems, as well our new
\NR algorithm which we adopted from our previous work on folksonomies and which
yields the best results, considering the trade-off between prediction accuracy
and runtime performance as well as its ability to generate personalized
recommendations. We also show, how the gathered inter-name relationships can be
used for meaningful result diversification of PageRank-based recommendation
systems.
  As all of the considered usage data is made publicly available, the present
work establishes baseline results, encouraging other researchers to implement
advanced recommendation systems for given names.},
  author = {Mitzlaff, Folke and Stumme, Gerd},
  interhash = {545658b6e337858f7865b51e46d1c7a6},
  intrahash = {41f92650f0f7d78366febc1832cedba9},
  note = {cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013},
  title = {Recommending Given Names},
  url = {http://arxiv.org/abs/1302.4412},
  year = 2013
}