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 - https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf M3 - 10.1145/2507157.2507222 KW - 2013 KW - bibsonomy KW - bookmarking KW - collaborative KW - core KW - evaluation KW - folkrank KW - folksonomy KW - graph KW - iteg KW - itegpub KW - l3s KW - recommender KW - social KW - tagging 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 - JOUR AU - Krause, Beate AU - Lerch, Hana AU - Hotho, Andreas AU - Roßnagel, Alexander AU - Stumme, Gerd T1 - Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy. JO - Informatik Spektrum PY - 2012/ VL - 35 IS - 1 SP - 12 EP - 23 UR - http://dblp.uni-trier.de/db/journals/insk/insk35.html#KrauseLHRS12 M3 - KW - 2012 KW - bibsonomy KW - datenschutz KW - info20 KW - itegpub KW - l3s KW - myown KW - privacy KW - tagging L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Benz, Dominik AU - Hotho, Andreas AU - Jäschke, Robert AU - Krause, Beate AU - Mitzlaff, Folke AU - Schmitz, Christoph AU - Stumme, Gerd T1 - The social bookmark and publication management system bibsonomy JO - The VLDB Journal PY - 2010/ VL - 19 IS - 6 SP - 849 EP - 875 UR - http://dx.doi.org/10.1007/s00778-010-0208-4 M3 - 10.1007/s00778-010-0208-4 KW - 2010 KW - BibSonomy KW - Journal KW - VLDB KW - VLDBJ KW - info20 KW - itegpub KW - l3s KW - myown KW - sitc L1 - SN - N1 - SpringerLink - The VLDB Journal, Volume 19, Number 6 N1 - AB - Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research. ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Strohmaier, Markus AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity T2 - Proceedings of the 19th International World Wide Web Conference (WWW 2010) PB - ACM CY - Raleigh, NC, USA PY - 2010/04 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf M3 - KW - bibsonomy KW - delicious KW - emerge KW - itegpub KW - l3s KW - myown KW - semantic KW - semantics KW - social KW - start KW - tagging KW - thinking KW - web L1 - SN - N1 - N1 - AB - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies. ER -