Mitzlaff, F.; Doerfel, S.; Hotho, A.; Jäschke, R. & Mueller, J.: Summary of the 15th Discovery Challenge: Recommending Given Names. 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: CEUR-WS, 2014 (1120), S. 7-24
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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.

Doerfel, S. & Jäschke, R.: An Analysis of Tag-Recommender Evaluation Procedures. Proceedings of the 7th ACM conference on Recommender systems. New York, NY, USA: ACM, 2013RecSys '13 , S. 343-346
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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.

Mueller, J.; Doerfel, S.; Becker, M.; Hotho, A. & Stumme, G.: Tag Recommendations for SensorFolkSonomies. Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China - October 12-16, 2013. Proceedings. ACM, 2013, S. New York, NY, USA

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.

Doerfel, S.; Jäschke, R.; Hotho, A. & Stumme, G.: Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation. Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web. New York, NY, USA: ACM, 2012RSWeb '12 , S. 9-16
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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.

Jannach, D.: Recommender systems : an introduction. New York: Cambridge University Press, 2011
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