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. ER - TY - CONF AU - Doerfel, Stephan AU - Jäschke, Robert A2 - T1 - An Analysis of Tag-Recommender Evaluation Procedures T2 - Proceedings of the 7th ACM conference on Recommender systems PB - ACM CY - New York, NY, USA PY - 2013/ M2 - VL - IS - SP - 343 EP - 346 UR - http://doi.acm.org/10.1145/2507157.2507222 M3 - 10.1145/2507157.2507222 KW - 2013 KW - BibSonomy KW - core KW - evaluation KW - iteg KW - itegpub KW - l3s KW - myown KW - recsys KW - tag L1 - SN - 978-1-4503-2409-0 N1 - N1 - AB - 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. ER - TY - CONF AU - Doerfel, Stephan AU - Jäschke, Robert A2 - T1 - An Analysis of Tag-Recommender Evaluation Procedures T2 - Proceedings of the 7th ACM conference on Recommender systems PB - ACM CY - New York, NY, USA PY - 2013/ M2 - VL - IS - SP - 343 EP - 346 UR - http://doi.acm.org/10.1145/2507157.2507222 M3 - 10.1145/2507157.2507222 KW - 2013 KW - BibSonomy KW - core KW - evaluation KW - myown KW - recsys KW - tag L1 - SN - 978-1-4503-2409-0 N1 - An analysis of tag-recommender evaluation procedures N1 - AB - 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. ER - TY - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - ACM CY - PY - 2013/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2013 KW - RecSys KW - everyaware KW - folksonomy KW - iteg KW - itegpub KW - l3s KW - myown KW - recommendation KW - rsweb KW - sensor KW - sitc KW - tag KW - widenoise L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - CEUR-WS CY - Aachen, Germany PY - 2013/ M2 - VL - 1066 IS - SP - EP - UR - http://ceur-ws.org/Vol-1066/ M3 - KW - 2013 KW - EveryAware KW - RecSys KW - inproceedings KW - iteg KW - itegpub KW - l3s KW - myown L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - ACM CY - PY - 2013/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2013 KW - RecSys KW - everyaware KW - folksonomy KW - myown KW - recommendation KW - rsweb KW - sensor KW - tag KW - widenoise L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Doerfel, Stephan AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation T2 - Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web PB - ACM CY - New York, NY, USA PY - 2012/ M2 - VL - IS - SP - 9 EP - 16 UR - http://doi.acm.org/10.1145/2365934.2365937 M3 - 10.1145/2365934.2365937 KW - 2012 KW - data KW - info20pub KW - itegpub KW - itemRecommendation KW - leveraging KW - metadata KW - myown KW - publication KW - reco KW - recsys KW - social L1 - SN - 978-1-4503-1638-5 N1 - Leveraging publication metadata and social data into FolkRank for scientific publication recommendation N1 - AB - 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. ER - TY - GEN AU - Kohavi, Ron A2 - T1 - Online Controlled Experiments: Introduction, Learnings, and Humbling Statistics JO - PB - AD - PY - 2012/10 VL - IS - SP - EP - UR - http://www.exp-platform.com/Pages/2012RecSys.aspx M3 - KW - 2012 KW - amazon KW - bing KW - evaluation KW - experiment KW - industry KW - keynote KW - online KW - recommender KW - recsys KW - statistics L1 - N1 - N1 - AB - The web provides an unprecedented opportunity to accelerate innovation by evaluating ideas quickly and accurately using controlled experiments (e.g., A/B tests and their generalizations). Whether for front-end user-interface changes, or backend recommendation systems and relevance algorithms, online controlled experiments are now utilized to make data-driven decisions at Amazon, Microsoft, eBay, Facebook, Google, Yahoo, Zynga, and at many other companies. While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale—thousands of experiments now—has taught us many lessons. We provide an introduction, share real examples, key learnings, cultural challenges, and humbling statistics. ER - TY - BOOK AU - Jannach, Dietmar A2 - T1 - Recommender systems : an introduction PB - Cambridge University Press AD - New York PY - 2011/ VL - IS - SP - EP - UR - http://www.amazon.de/Recommender-Systems-Introduction-Dietmar-Jannach/dp/0521493366/ref=sr_1_1?ie=UTF8&qid=1356099943&sr=8-1 M3 - KW - introduction KW - recommender KW - recsys L1 - SN - 9780521493369 0521493366 N1 - Recommender Systems: An Introduction: Amazon.de: Dietmar Jannach, Markus Zanker, Alexander Felfernig, Gerhard Friedich: Englische Bücher N1 - AB - ER - TY - CONF AU - Said, Alan AU - Berkovsky, Shlomo AU - Luca, Ernesto W. De A2 - T1 - Putting things in context: Challenge on Context-Aware Movie Recommendation T2 - Proceedings of the Workshop on Context-Aware Movie Recommendation PB - ACM CY - New York, NY, USA PY - 2010/ M2 - VL - IS - SP - 2 EP - 6 UR - http://doi.acm.org/10.1145/1869652.1869665 M3 - 10.1145/1869652.1869665 KW - challenge KW - movie KW - recommender KW - recsys L1 - SN - 978-1-4503-0258-6 N1 - N1 - AB - The Challenge on Context-Aware Movie Recommendation (CAMRa) was conducted as part of a join event on Context-Awareness in Recommender Systems at the 2010 ACM Recommender Systems conference. The challenge focused on three context-aware recommendation tasks: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real world online movie recommendation communities and competed against each other for obtaining the highest recommendation accuracy for each task. The datasets contained contextual features, such as mood, plot annotation, social network, and comments, normally not available in movie recommendation datasets. Over 40 teams from 20 countries participated in the challenge. Their participation was summarized by 10 papers accepted to the CAMRa workshop. ER - TY - CONF AU - Jäschke, Robert AU - Eisterlehner, Folke AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Testing and Evaluating Tag Recommenders in a Live System T2 - RecSys '09: Proceedings of the third ACM Conference on Recommender Systems PB - ACM CY - New York, NY, USA PY - 2009/ M2 - VL - IS - SP - 369 EP - 372 UR - http://www.kde.cs.uni-kassel.de/pub/pdf/jaeschke2009testing.pdf M3 - 10.1145/1639714.1639790 KW - 2009 KW - bibsonomy KW - conference KW - framework KW - myown KW - recommender KW - recsys L1 - SN - 978-1-60558-435-5 N1 - N1 - AB - The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods. ER -