Artikel in Tagungsbänden
Tag Recommendations for SensorFolkSonomies.
In:
Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China - October 12-16, 2013. Proceedings, Band 1066.
CEUR-WS, Aachen, Germany, 2013.
Juergen Mueller, Stephan Doerfel, Martin Becker, Andreas Hotho und Gerd Stumme.
[doi]
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[BibTeX]
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.
A Comparison of Content-Based Tag Recommendations in Folksonomy Systems.
In: K. E. Wolff, D. E. Palchunov, N. G. Zagoruiko und U. Andelfinger
(Herausgeber):
Knowledge Processing and Data Analysis, Band 6581, Reihe Lecture Notes in Computer Science, Seiten 136-149.
Springer, Berlin/Heidelberg, 2011.
Jens Illig, Andreas Hotho, Robert Jäschke und Gerd Stumme.
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Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.
Dissertation
Formal concept analysis and tag recommendations in collaborative tagging systems.
Doktorarbeit, [Amsterdam], 2011.
Robert Jäschke.
[doi]
[BibTeX]
Artikel in Tagungsbänden
I tag, you tag: translating tags for advanced user models..
In: B. D. Davison, T. Suel, N. Craswell und B. Liu
(Herausgeber):
WSDM, Seiten 71-80.
ACM, 2010.
Robert Wetzker, Carsten Zimmermann, Christian Bauckhage und Sahin Albayrak.
[doi]
[BibTeX]
Sonstiges
The Role of Tag Suggestions in Folksonomies.
2009. cite arxiv:0903.1788
.
Dirk Bollen und Harry Halpin.
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Most tagging systems support the user in the tag selection process by
providing tag suggestions, or recommendations, based on a popularity
measurement of tags other users provided when tagging the same resource. In
this paper we investigate the influence of tag suggestions on the emergence of
power law distributions as a result of collaborative tag behavior. Although
previous research has already shown that power laws emerge in tagging systems,
the cause of why power law distributions emerge is not understood empirically.
The majority of theories and mathematical models of tagging found in the
literature assume that the emergence of power laws in tagging systems is mainly
driven by the imitation behavior of users when observing tag suggestions
provided by the user interface of the tagging system. This imitation behavior
leads to a feedback loop in which some tags are reinforced and get more popular
which is also known as the `rich get richer' or a preferential attachment
model. We present experimental results that show that the power law
distribution forms regardless of whether or not tag suggestions are presented
to the users. Furthermore, we show that the real effect of tag suggestions is
rather subtle; the resulting power law distribution is `compressed' if tag
suggestions are given to the user, resulting in a shorter long tail and a
`compressed' top of the power law distribution. The consequences of this
experiment show that tag suggestions by themselves do not account for the
formation of power law distributions in tagging systems.
Buchbeiträge
Persönliches Tag Gardening mit tagCare..
In:
M. Ockenfeld (Herausgeber):
Generation international - die Zukunft von Information, Wissenschaft und Profession. Proceedings der 31. Online-Tagung der Germany., Seiten 117-128.
DGI Frankfurt a. M., 2009.
Frankfurt am Main: DGI
C. Dittmann, M. Dittmann, I. Peters und K. Weller.
[doi]
[BibTeX]
Artikel in Tagungsbänden
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects..
In: J. Allan, J. A. Aslam, M. Sanderson, C. Zhai und J. Zobel
(Herausgeber):
SIGIR, Seiten 540-547.
ACM, 2009.
Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen und Can Wang.
[doi]
[BibTeX]
Tagommenders: connecting users to items through tags..
In: J. Quemada, G. León, Y. S. Maarek und W. Nejdl
(Herausgeber):
WWW, Seiten 671-680.
ACM, 2009.
Shilad Sen, Jesse Vig und John Riedl.
[doi]
[BibTeX]
Sonstiges
Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems.
2008.
Ciro Cattuto, Dominik Benz, Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.
Artikel in Tagungsbänden
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems.
In:
The Semantic Web - ISWC 2008, Band 5318, Reihe Lecture Notes in Computer Science, Seiten 615-631.
Springer Berlin / Heidelberg, 2008.
Ciro Cattuto, Dominik Benz, Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.
Social tag prediction.
In:
SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seiten 531-538.
ACM, New York, NY, USA, 2008.
Paul Heymann, Daniel Ramage und Hector Garcia-Molina.
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In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.
The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies.
In:
Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications, Seiten 128-137.
Dublin Core Metadata Initiative, Berlin, Deutschland, 2008.
Hak Lae Kim, Simon Scerri, John G. Breslin, Stefan Decker und Hong Gee Kim.
[BibTeX]
Artikel in Zeitschriften
The folksonomy tag cloud: when is it useful?.
Journal of Information Science, 34(1):15-29, 2008.
James Sinclair und Michael Cardew-Hall.
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The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding information. We conducted an experiment, giving participants the option of using a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means of navigation for a folksonomy-based dataset.
Artikel in Tagungsbänden
A sparse gaussian processes classification framework for fast tag suggestions.
In:
CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining, Seiten 93-102.
ACM, New York, NY, USA, 2008.
Yang Song, Lu Zhang und C. Lee Giles.
[doi]
[BibTeX]
Real-time automatic tag recommendation.
In:
SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, Seiten 515-522.
ACM, New York, NY, USA, 2008.
Yang Song, Ziming Zhuang, Huajing Li, Qiankun Zhao, Jia Li, Wang-Chien Lee und C. Lee Giles.
[doi]
[BibTeX]
Tag recommendations based on tensor dimensionality reduction.
In:
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems, Seiten 43-50.
ACM, New York, NY, USA, 2008.
Panagiotis Symeonidis, Alexandros Nanopoulos und Yannis Manolopoulos.
[doi]
[BibTeX]
Understanding the Semantics of Ambiguous Tags in Folksonomies.
In: L. Chen, P. Cudré-Mauroux, P. Haase, A. Hotho und E. Ong
(Herausgeber):
ESOE, Band 292, Reihe CEUR Workshop Proceedings, Seiten 108-121.
CEUR-WS.org, 2007.
Ching man Au Yeung, Nicholas Gibbins und Nigel Shadbolt.
[BibTeX]
Artikel in Zeitschriften
The folksonomy tag cloud: When is it useful?.
Journal of Information Science:016555150607808, 2007.
J. Sinclair und M. Cardew-Hall.
[BibTeX]
Artikel in Tagungsbänden
Improving Tag-Clouds as Visual Information Retrieval Interfaces.
In:
InScit2006: International Conference on Multidisciplinary Information Sciences and Technologies.
2006.
Y. Hassan-Montero und V. Herrero-Solana.
[doi]
[Kurzfassung]
[BibTeX]
Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to re-finding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud�s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.
tagging, communities, vocabulary, evolution.
In:
CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, Seiten 181-190.
ACM, New York, NY, USA, 2006.
Shilad Sen, Shyong K. Lam, Al Mamunur Rashid, Dan Cosley, Dan Frankowski, Jeremy Osterhouse, F. Maxwell Harper und John Riedl.
[doi]
[Kurzfassung]
[BibTeX]
A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.