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  -

TY  - JOUR
AU  - Benetka, Gerhard
AU  - Przyborski, Aglaja
T1  - Der Bologna-Prozess als Chance für eine praxeologische Wende in der Psychologieausbildung
JO  - 
PY  - 2006/
VL  - 
IS  - 
SP  - 
EP  - 
UR  - http://www.sfu.ac.at/psychologie/download/pioe.pdf
M3  - 
KW  - Departementsleiters
KW  - Methodenleiterin
KW  - PrivatUniversität
KW  - Psychologie
KW  - Psychologiestudiums
KW  - Publikation
KW  - Qualitativen
KW  - Sigmund-Freud
KW  - Start
KW  - Wien
KW  - an
KW  - der
KW  - des
KW  - und
KW  - zum
L1  - 
SN  - 
N1  - Der erste Testeintrag von mir (Michael-Josef)
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Schein, Andrew I.
AU  - Popescul, Alexandrin
AU  - Ungar, Lyle H.
AU  - Pennock, David M.
A2  - 
T1  - Methods and Metrics for Cold-start Recommendations
T2  - Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
PB  - ACM
CY  - New York, NY, USA
PY  - 2002/
M2  - 
VL  - 
IS  - 
SP  - 253
EP  - 260
UR  - http://doi.acm.org/10.1145/564376.564421
M3  - 10.1145/564376.564421
KW  - cold
KW  - croc
KW  - groc
KW  - metrics
KW  - recommender
KW  - start
L1  - 
SN  - 1-58113-561-0
N1  - Methods and metrics for cold-start recommendations
N1  - 
AB  - We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on the  cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.
ER  -