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 - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Wolff, Karl Erich A2 - Palchunov, Dmitry E. A2 - Zagoruiko, Nikolay G. A2 - Andelfinger, Urs T1 - A Comparison of Content-Based Tag Recommendations in Folksonomy Systems T2 - Knowledge Processing and Data Analysis PB - Springer CY - Berlin/Heidelberg PY - 2011/ M2 - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 M3 - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - content KW - folksonomy KW - myown KW - recommendations KW - recommender KW - tag L1 - SN - 978-3-642-22139-2 N1 - N1 - AB - 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. ER - TY - THES AU - Jäschke, Robert T1 - Formal concept analysis and tag recommendations in collaborative tagging systems PY - 2011/ PB - SP - EP - UR - http://www.worldcat.org/search?qt=worldcat_org_all&q=9783898383325 M3 - KW - bibsonomy KW - bookmarking KW - dissertation KW - fca KW - recommender KW - social KW - tag KW - tagging KW - taggingsurvey L1 - N1 - N1 - AB - ER - TY - CONF AU - Wetzker, Robert AU - Zimmermann, Carsten AU - Bauckhage, Christian AU - Albayrak, Sahin A2 - Davison, Brian D. A2 - Suel, Torsten A2 - Craswell, Nick A2 - Liu, Bing T1 - I tag, you tag: translating tags for advanced user models. T2 - WSDM PB - ACM CY - PY - 2010/ M2 - VL - IS - SP - 71 EP - 80 UR - http://dblp.uni-trier.de/db/conf/wsdm/wsdm2010.html#WetzkerZBA10 M3 - KW - abbildung KW - folksonomy KW - recommender KW - tag KW - tagging KW - taggingsurvey KW - translation L1 - SN - 978-1-60558-889-6 N1 - dblp N1 - AB - ER - TY - GEN AU - Bollen, Dirk AU - Halpin, Harry A2 - T1 - The Role of Tag Suggestions in Folksonomies JO - PB - AD - PY - 2009/ VL - IS - SP - EP - UR - http://arxiv.org/abs/0903.1788 M3 - KW - folksonomy KW - recommedation KW - tag KW - toread L1 - N1 - The Role of Tag Suggestions in Folksonomies N1 - AB - 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.
ER - TY - CHAP AU - Dittmann, C. AU - Dittmann, M. AU - Peters, I. AU - Weller, K. A2 - Ockenfeld, M. T1 - Persönliches Tag Gardening mit tagCare. T2 - Generation international - die Zukunft von Information, Wissenschaft und Profession. Proceedings der 31. Online-Tagung der Germany. PB - DGI Frankfurt a. M. CY - PY - 2009/ VL - IS - SP - 117 EP - 128 UR - http://wwwalt.phil-fak.uni-duesseldorf.de/infowiss/content/mitarbeiter/peters.php M3 - KW - gardening KW - semantics KW - tag L1 - SN - N1 - Dr. I. Peters - Wissenschaftliche Angestellte || Informationswissenschaft Heinrich-Heine-Universität Düsseldorf N1 - AB - ER - TY - CONF AU - Guan, Ziyu AU - Bu, Jiajun AU - Mei, Qiaozhu AU - Chen, Chun AU - Wang, Can A2 - Allan, James A2 - Aslam, Javed A. A2 - Sanderson, Mark A2 - Zhai, ChengXiang A2 - Zobel, Justin T1 - Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. T2 - SIGIR PB - ACM CY - PY - 2009/ M2 - VL - IS - SP - 540 EP - 547 UR - http://www-personal.umich.edu/~qmei/pub/sigir09-tag.pdf M3 - KW - recommendation KW - tag KW - toread L1 - SN - 978-1-60558-483-6 N1 - dblp N1 - AB - ER - TY - CONF AU - Sen, Shilad AU - Vig, Jesse AU - Riedl, John A2 - Quemada, Juan A2 - León, Gonzalo A2 - Maarek, Yoëlle S. A2 - Nejdl, Wolfgang T1 - Tagommenders: connecting users to items through tags. T2 - WWW PB - ACM CY - PY - 2009/ M2 - VL - IS - SP - 671 EP - 680 UR - http://dblp.uni-trier.de/db/conf/www/www2009.html#SenVR09 M3 - KW - item KW - recommender KW - tag KW - toread KW - user L1 - SN - 978-1-60558-487-4 N1 - dblp N1 - AB - ER - TY - GEN AU - Cattuto, Ciro AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems JO - PB - AD - PY - 2008/ VL - IS - SP - EP - UR - http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045 M3 - KW - 2008 KW - analysis KW - learning KW - myown KW - ol KW - ontology KW - semantic KW - similarity KW - tag L1 - N1 - [0805.2045] Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems N1 - AB - 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. ER - TY - CONF AU - Cattuto, Ciro AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantic Grounding of Tag Relatedness in Social Bookmarking Systems T2 - The Semantic Web - ISWC 2008 PB - Springer Berlin / Heidelberg CY - PY - 2008/ M2 - VL - 5318 IS - SP - 615 EP - 631 UR - http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf M3 - 10.1007/978-3-540-88564-1_39 KW - 2008 KW - folksonomy KW - grounding KW - iswc2008 KW - myown KW - semantic KW - sw KW - tag KW - tagging KW - taggingsurvey KW - webzu L1 - SN - 978-3-540-88563-4 N1 - SpringerLink - Buchkapitel N1 - AB - 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. ER - TY - CONF AU - Heymann, Paul AU - Ramage, Daniel AU - Garcia-Molina, Hector A2 - T1 - Social tag prediction T2 - SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 531 EP - 538 UR - http://portal.acm.org/citation.cfm?id=1390334.1390425 M3 - http://doi.acm.org/10.1145/1390334.1390425 KW - folksonomy KW - prediction KW - recommender KW - social KW - tag KW - tagging KW - taggingsurvey KW - toread L1 - SN - 978-1-60558-164-4 N1 - Social tag prediction N1 - AB - 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. ER - TY - CONF AU - Kim, Hak Lae AU - Scerri, Simon AU - Breslin, John G. AU - Decker, Stefan AU - Kim, Hong Gee A2 - T1 - The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies T2 - Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications PB - Dublin Core Metadata Initiative CY - Berlin, Deutschland PY - 2008/ M2 - VL - IS - SP - 128 EP - 137 UR - M3 - KW - folksonomy KW - ontology KW - semantic KW - tag KW - tagging KW - taggingsurvey KW - toread L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Sinclair, James AU - Cardew-Hall, Michael T1 - The folksonomy tag cloud: when is it useful? JO - Journal of Information Science PY - 2008/ VL - 34 IS - 1 SP - 15 EP - 29 UR - http://jis.sagepub.com/cgi/content/abstract/34/1/15 M3 - 10.1177/0165551506078083 KW - analysis KW - cloud KW - folksonomy KW - tag KW - toread L1 - SN - N1 - The folksonomy tag cloud: when is it useful? -- Sinclair and Cardew-Hall 34 (1): 15 -- Journal of Information Science N1 - AB - 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.
ER - TY - CONF AU - Song, Yang AU - Zhang, Lu AU - Giles, C. Lee A2 - T1 - A sparse gaussian processes classification framework for fast tag suggestions T2 - CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 93 EP - 102 UR - http://portal.acm.org/citation.cfm?id=1458098 M3 - http://doi.acm.org/10.1145/1458082.1458098 KW - bibsonomy KW - bookmarking KW - classification KW - dataset KW - ml KW - recommender KW - social KW - tag KW - tagging KW - taggingsurvey KW - toread L1 - SN - 978-1-59593-991-3 N1 - A sparse gaussian processes classification framework for fast tag suggestions N1 - AB - ER - TY - CONF AU - Song, Yang AU - Zhuang, Ziming AU - Li, Huajing AU - Zhao, Qiankun AU - Li, Jia AU - Lee, Wang-Chien AU - Giles, C. Lee A2 - T1 - Real-time automatic tag recommendation T2 - SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 515 EP - 522 UR - http://portal.acm.org/citation.cfm?id=1390334.1390423 M3 - http://doi.acm.org/10.1145/1390334.1390423 KW - recommender KW - tag KW - toread L1 - SN - 978-1-60558-164-4 N1 - Real-time automatic tag recommendation N1 - AB - ER - TY - CONF AU - Symeonidis, Panagiotis AU - Nanopoulos, Alexandros AU - Manolopoulos, Yannis A2 - T1 - Tag recommendations based on tensor dimensionality reduction T2 - RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems PB - ACM CY - New York, NY, USA PY - 2008/ M2 - VL - IS - SP - 43 EP - 50 UR - http://portal.acm.org/citation.cfm?id=1454017 M3 - http://doi.acm.org/10.1145/1454008.1454017 KW - folksonomy KW - recommender KW - tag KW - tensor KW - toread L1 - SN - 978-1-60558-093-7 N1 - Tag recommendations based on tensor dimensionality reduction N1 - AB - ER - TY - CONF AU - man Au Yeung, Ching AU - Gibbins, Nicholas AU - Shadbolt, Nigel A2 - Chen, Liming A2 - Cudré-Mauroux, Philippe A2 - Haase, Peter A2 - Hotho, Andreas A2 - Ong, Ernie T1 - Understanding the Semantics of Ambiguous Tags in Folksonomies T2 - ESOE PB - CEUR-WS.org CY - PY - 2007/ M2 - VL - 292 IS - SP - 108 EP - 121 UR - M3 - KW - disambiguation KW - semantics KW - tag L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Sinclair, J. AU - Cardew-Hall, M. T1 - The folksonomy tag cloud: When is it useful? JO - Journal of Information Science PY - 2007/ VL - IS - SP - EP - UR - M3 - KW - cloud KW - folksonomy KW - kdubiq KW - ranking KW - search KW - summerschool KW - tag KW - tagging KW - taggingsurvey L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Hassan-Montero, Y. AU - Herrero-Solana, V. A2 - T1 - Improving Tag-Clouds as Visual Information Retrieval Interfaces T2 - InScit2006: International Conference on Multidisciplinary Information Sciences and Technologies PB - CY - PY - 2006/ M2 - VL - IS - SP - EP - UR - http://nosolousabilidad.com/hassan/improving_tagclouds.pdf M3 - KW - clouds KW - dataset KW - del.icio.us KW - information KW - tag KW - tagging KW - taggingsurvey KW - toread KW - visual L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Sen, Shilad AU - Lam, Shyong K. AU - Rashid, Al Mamunur AU - Cosley, Dan AU - Frankowski, Dan AU - Osterhouse, Jeremy AU - Harper, F. Maxwell AU - Riedl, John A2 - T1 - tagging, communities, vocabulary, evolution T2 - CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work PB - ACM CY - New York, NY, USA PY - 2006/ M2 - VL - IS - SP - 181 EP - 190 UR - http://portal.acm.org/citation.cfm?id=1180904 M3 - http://doi.acm.org/10.1145/1180875.1180904 KW - analysis KW - evolution KW - kdubiq KW - recommender KW - summerschool KW - tag KW - tagging KW - taggingsurvey KW - vocabulary L1 - SN - 1-59593-249-6 N1 - tagging, communities, vocabulary, evolution N1 - AB - 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. ER -