@inproceedings{mitzlaff2014summary,
  abstract = {The 15th ECML PKDD Discovery Challenge centered around the recommendation
 of given names. Participants of the challenge implemented algorithms
 that were tested both offline - on data collected by the name search
 engine Nameling - and online within Nameling. Here, we describe both
 tasks in detail and discuss the publicly available datasets. We motivate
 and explain the chosen evaluation of the challenge, and we summarize
 the different approaches applied to the name recommendation tasks.
 Finally, we present the rankings and winners of the offline and the
 online phase.},
  address = {Aachen, Germany},
  author = {Mitzlaff, Folke and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Mueller, Juergen},
  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},
  interhash = {6945009ac2fd770a84c47f2a0e192802},
  intrahash = {e279965dffa803ee660e4026fe48340a},
  issn = {1613-0073},
  pages = {7--24},
  publisher = {CEUR-WS},
  title = {Summary of the 15th Discovery Challenge: Recommending Given Names},
  url = {http://ceur-ws.org/Vol-1120/},
  volume = 1120,
  year = 2014
}

@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
}

@inproceedings{doerfel2013analysis,
  abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.},
  acmid = {2507222},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert},
  booktitle = {Proceedings of the 7th ACM conference on Recommender systems},
  doi = {10.1145/2507157.2507222},
  interhash = {3eaf2beb1cdad39b7c5735a82c3338dd},
  intrahash = {a73213a865503252caa4b28e88a77108},
  isbn = {978-1-4503-2409-0},
  location = {Hong Kong, China},
  numpages = {4},
  pages = {343--346},
  publisher = {ACM},
  series = {RecSys '13},
  title = {An Analysis of Tag-Recommender Evaluation Procedures},
  url = {http://doi.acm.org/10.1145/2507157.2507222},
  year = 2013
}

@book{jannach2011recommender,
  address = {New York},
  author = {Jannach, Dietmar},
  interhash = {a6610e8a27191994e0b85d73c26bdce8},
  intrahash = {21abf93de5cf457f735f5beddec928a2},
  isbn = {9780521493369 0521493366},
  publisher = {Cambridge University Press},
  refid = {645789647},
  title = {Recommender systems : an introduction},
  url = {http://www.amazon.de/Recommender-Systems-Introduction-Dietmar-Jannach/dp/0521493366/ref=sr_1_1?ie=UTF8&qid=1356099943&sr=8-1},
  year = 2011
}

@inproceedings{doerfel2012leveraging,
  abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.},
  acmid = {2365937},
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365937},
  interhash = {beb2c81daf975eeed6e01e1b412196b1},
  intrahash = {d3e6fa8023b173228a959914affc8d73},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  numpages = {8},
  pages = {9--16},
  publisher = {ACM},
  series = {RSWeb '12},
  title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation},
  url = {http://doi.acm.org/10.1145/2365934.2365937},
  year = 2012
}