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 - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-1-4614-1894-8 DO - 10.1007/978-1-4614-1894-8 KW - 2012 KW - bookmarking KW - collaborative KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommender KW - social KW - tagging KW - tagging KW - 2012 L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models. ER - TY - CHAP AU - Jäschke, Robert AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - Pazos Arias, José J. A2 - Fernández Vilas, Ana A2 - Díaz Redondo, Rebeca P. T1 - Challenges in Tag Recommendations for Collaborative Tagging Systems T2 - Recommender Systems for the Social Web PB - Springer C1 - Berlin/Heidelberg PY - 2012/ VL - 32 IS - SP - 65 EP - 87 UR - http://dx.doi.org/10.1007/978-3-642-25694-3_3 DO - 10.1007/978-3-642-25694-3_3 KW - 2012 KW - bookmarking KW - challenge KW - collaborative KW - dc09 KW - discovery KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommender KW - rsdc08 KW - social KW - tagging L1 - SN - 978-3-642-25694-3 N1 - N1 - AB - Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area. 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 C1 - Berlin/Heidelberg PY - 2011/ CY - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 DO - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - content KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommendations KW - recommender KW - tag KW - tagorapub 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 - JOUR AU - Benz, Dominik AU - Hotho, Andreas AU - Jäschke, Robert AU - Krause, Beate AU - Stumme, Gerd T1 - Query Logs as Folksonomies JO - Datenbank-Spektrum PY - 2010/06 VL - 10 IS - 1 SP - 15 EP - 24 UR - http://dx.doi.org/10.1007/s13222-010-0004-8 DO - KW - 2010 KW - folksonomies KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - log KW - logs KW - logsonomy KW - myown L1 - SN - N1 - N1 - AB - Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well. ER - TY - CONF AU - Cattuto, Ciro AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems T2 - Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3) PB - C1 - Patras, Greece PY - 2008/07 CY - VL - IS - SP - EP - UR - http://olp.dfki.de/olp3/ DO - KW - 2.0 KW - 2008 KW - collaborative KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - semantic KW - systems KW - tagging KW - web KW - web2.0 L1 - SN - N1 - Publications of Gerd Stumme 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 - Jäschke, Robert AU - Krause, Beate AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy -- A Search Engine Folksonomy T2 - Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) PB - AAAI Press C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf DO - KW - 2008 KW - engine KW - folksonomies KW - folksonomy KW - itegpub KW - logsonomies KW - logsonomy KW - myown KW - search KW - tagorapub L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - In social bookmarking systems users describe bookmarks
by keywords called tags. The structure behind
these social systems, called folksonomies, can be
viewed as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows specific
structural properties that explain its growth and the possibility
of serendipitous exploration.
Search engines filter the vast information of the web.
Queries describe a user’s information need. In response
to the displayed results of the search engine, users click
on the links of the result page as they expect the answer
to be of relevance. The clickdata can be represented as a
folksonomy in which queries are descriptions of clicked
URLs. This poster analyzes the topological characteristics
of the resulting tripartite hypergraph of queries,
users and bookmarks of two query logs and compares it
two a snapshot of the folksonomy del.icio.us. ER - TY - CONF AU - Krause, Beate AU - Hotho, Andreas AU - Stumme, Gerd A2 - Macdonald, Craig A2 - Ounis, Iadh A2 - Plachouras, Vassilis A2 - Ruthven, Ian A2 - White, Ryen W. T1 - A Comparison of Social Bookmarking with Traditional Search T2 - Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008 PB - Springer C1 - Heidelberg PY - 2008/ CY - VL - 4956 IS - SP - 101 EP - 113 UR - DO - KW - 2008 KW - bookmarking KW - comparison KW - folksonomies KW - folksonomy KW - itegpub KW - logsonomies KW - myown KW - search KW - social KW - tagorapub L1 - SN - N1 - N1 - AB - Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s
data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users.
In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.
Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems
to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space
retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system. ER - TY - CONF AU - Krause, Beate AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy - Social Information Retrieval with Logdata T2 - HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia PB - ACM C1 - New York, NY, USA PY - 2008/ CY - VL - IS - SP - 157 EP - 166 UR - http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia DO - http://doi.acm.org/10.1145/1379092.1379123 KW - 2.0 KW - 2008 KW - analysis KW - folksonomy KW - information KW - itegpub KW - logsonomy KW - myown KW - network KW - retrieval KW - search KW - social KW - tagorapub KW - web KW - web2.0 KW - web20 L1 - SN - 978-1-59593-985-2 N1 - N1 - AB - Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics. ER - TY - CONF AU - Krause, Beate AU - Schmitz, Christoph AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems T2 - Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web PB - C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf DO - KW - 2.0 KW - 2008 KW - bookmarking KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - spam KW - systems KW - tagger KW - tagorapub KW - web KW - web2.0 L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - JOUR AU - Cattuto, Ciro AU - Schmitz, Christoph AU - Baldassarri, Andrea AU - Servedio, Vito D. P. AU - Loreto, Vittorio AU - Hotho, Andreas AU - Grahl, Miranda AU - Stumme, Gerd T1 - Network Properties of Folksonomies JO - AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering'' PY - 2007/ VL - 20 IS - 4 SP - 245 EP - 262 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf DO - KW - 2007 KW - emergent KW - fca KW - folksonomies KW - folksonomy KW - itegpub KW - l3s KW - myown KW - network KW - semantics KW - seminar2009 KW - tagorapub L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - Hinneburg, Alexander T1 - Conceptual Clustering of Social Bookmark Sites T2 - Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007) PB - Martin-Luther-Universität Halle-Wittenberg C1 - PY - 2007/10 CY - VL - IS - SP - 50 EP - 54 UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf DO - KW - 2007 KW - Social KW - bookmark KW - bookmarking KW - clustering KW - collaborative KW - conceptual KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - tagging KW - tagorapub L1 - SN - 978-3-86010-907-6 N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Conceptual Clustering of Social Bookmarking Sites T2 - 7th International Conference on Knowledge Management (I-KNOW '07) PB - Know-Center C1 - Graz, Austria PY - 2007/10 CY - VL - IS - SP - 356 EP - 364 UR - DO - KW - 2007 KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - sites KW - social KW - tagging KW - tagorapub L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the
system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system. ER - TY - CONF AU - Jaeschke, Robert AU - Marinho, Leandro AU - Hotho, Andreas AU - Schmidt-Thieme, Lars AU - Stumme, Gerd A2 - Hinneburg, Alexander T1 - Tag Recommendations in Folksonomies T2 - Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007) PB - Martin-Luther-Universität Halle-Wittenberg C1 - PY - 2007/10 CY - VL - IS - SP - 13 EP - 20 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf DO - KW - 2007 KW - bookmarking KW - collaborative KW - filtering KW - folksonomy KW - itegpub KW - l3s KW - myown KW - recommender KW - social L1 - SN - 978-3-86010-907-6 N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Schmitz, Christoph AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd AU - Catutto, Ciro AU - Baldassarri, Andrea AU - Loreto, Vittorio AU - Servedio, Vito D. P. A2 - T1 - Network Properties of Folksonomies T2 - Proc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization'' PB - C1 - Banff PY - 2007/05 CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/schmitz07network.pdf DO - KW - 2007 KW - emergent KW - fca KW - folksonomy KW - folksononomies KW - itegpub KW - l3s KW - myown KW - semantics KW - smallworld KW - sna KW - socialnetwork L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - de Moor, Aldo A2 - Polovina, Simon A2 - Delugach, Harry T1 - BibSonomy: A Social Bookmark and Publication Sharing System T2 - Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures PB - Aalborg Universitetsforlag C1 - Aalborg PY - 2006/ CY - VL - IS - SP - 87 EP - 102 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf DO - KW - 2006 KW - FCA KW - OntologyHandbook KW - bibsonomy KW - bookmarking KW - folksonomy KW - iccs KW - l3s KW - myown KW - nepomuk KW - social KW - tagorapub L1 - SN - 87-7307-769-0 N1 - Publications of Gerd Stumme N1 - AB - Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies and briefly describe
our own system BibSonomy, which allows for sharing both bookmarks
and publication references in a kind of personal library. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Hochberger, Christian A2 - Liskowsky, Rüdiger T1 - Emergent Semantics in BibSonomy T2 - Informatik 2006 -- Informatik für Menschen. Band 2 PB - Gesellschaft für Informatik C1 - Bonn PY - 2006/october CY - VL - P-94 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf DO - KW - 2006 KW - UniK KW - bibsonomy KW - emergence KW - emergent KW - folksonomy KW - hotho KW - itegpub KW - jaeschke KW - l3s KW - myown KW - nepomuk KW - schmitz KW - semantics KW - stumme KW - tagorapub L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. The reason for their immediate success is the
fact that no specific skills are needed for participating. In this
paper we specify a formal model for folksonomies, briefly describe
our own system BibSonomy,
which allows for sharing both bookmarks and
publication references,
and discuss first steps towards emergent semantics. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Information Retrieval in Folksonomies: Search and Ranking T2 - The Semantic Web: Research and Applications PB - Springer C1 - Heidelberg PY - 2006/06 CY - VL - 4011 IS - SP - 411 EP - 426 UR - DO - KW - 2006 KW - FCA KW - IR KW - OntologyHandbook KW - folkrank KW - folksonomy KW - information KW - informationretrieval KW - itegpub KW - mimose KW - myown KW - pagerank KW - ranking KW - retrieval L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Batagelj, V. A2 - Bock, H.-H. A2 - Ferligoj, A. A2 - Žiberna, A. T1 - Mining Association Rules in Folksonomies T2 - Data Science and Classification. Proceedings of the 10th IFCS Conf. PB - Springer C1 - Heidelberg PY - 2006/07 CY - VL - IS - SP - 261 EP - 270 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf DO - KW - 2006 KW - analysis KW - fca KW - folksonomies KW - folksonomy KW - l3s KW - myown KW - nepomuk KW - network KW - semantic L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system. ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Batagelj, V. A2 - Bock, H.-H. A2 - Ferligoj, A. A2 - v Ziberna, A. T1 - Mining Association Rules in Folksonomies T2 - Data Science and Classification: Proc. of the 10th IFCS Conf. PB - Springer C1 - Berlin, Heidelberg PY - 2006/ CY - VL - IS - SP - 261 EP - 270 UR - DO - KW - 2006 KW - FCA KW - OntologyHandbook KW - association KW - folksonomy KW - itegpub KW - myown KW - rule L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER -