@incollection{lorince2015analysis,
  abstract = {In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do },
  author = {Lorince, Jared and Joseph, Kenneth and Todd, PeterM.},
  booktitle = {Social Computing, Behavioral-Cultural Modeling, and Prediction},
  doi = {10.1007/978-3-319-16268-3_15},
  editor = {Agarwal, Nitin and Xu, Kevin and Osgood, Nathaniel},
  interhash = {b6f817ca50d1c44886c9ed58facbf592},
  intrahash = {1485f6521c6ae2db520d1a7c3c429f07},
  isbn = {978-3-319-16267-6},
  language = {English},
  pages = {141-152},
  publisher = {Springer International Publishing},
  series = {Lecture Notes in Computer Science},
  title = {Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids?},
  url = {http://dx.doi.org/10.1007/978-3-319-16268-3_15},
  volume = 9021,
  year = 2015
}

@inproceedings{lorince2014supertagger,
  author = {Lorince, Jared and Zorowitz, Sam and Murdock, Jaimie and Todd, Peter},
  interhash = {4af29810e9c882dc18f560527c65de2f},
  intrahash = {014abc7dc30e38859c5e8605dce1a8f6},
  title = {“Supertagger” Behavior in Building Folksonomies},
  year = 2014
}

@inproceedings{heckner2009personal,
  address = {San Jose, CA, USA},
  author = {Heckner, Markus and Heilemann, Michael and Wolff, Christian},
  booktitle = {Int'l AAAI Conference on Weblogs and Social Media (ICWSM)},
  interhash = {f954e699dc6ca2d0abbe5f6ebe166dc7},
  intrahash = {d1074484ea350ad88400fe4fc6984874},
  month = may,
  title = {Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems},
  year = 2009
}

@techreport{doerfel2014course,
  abstract = {Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior.},
  author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  interhash = {65f287480af20fc407f7d26677f17b72},
  intrahash = {e360f0bd207806e72305efe16491ebe3},
  note = {cite arxiv:1401.0629},
  title = {Of course we share! Testing Assumptions about Social Tagging Systems},
  url = {http://arxiv.org/abs/1401.0629},
  year = 2014
}

@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@inproceedings{mueller2013recommendations,
  abstract = {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.},
  author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings},
  interhash = {23d1cf49208d9a0c8b883dc69d4e444d},
  intrahash = {6190d6064dfdb3b8d71f2898539e993e},
  note = {accepted for publication},
  pages = {New York, NY, USA},
  publisher = {ACM},
  title = {Tag Recommendations for SensorFolkSonomies},
  year = 2013
}

@inproceedings{abrams1998information,
  acmid = {274651},
  address = {New York, NY, USA},
  author = {Abrams, David and Baecker, Ron and Chignell, Mark},
  booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
  doi = {10.1145/274644.274651},
  interhash = {fbb2704604de0954b432c8615a0abf5b},
  intrahash = {a9a25a144cec844bcd7daeace4a548aa},
  isbn = {0-201-30987-4},
  location = {Los Angeles, California, USA},
  numpages = {8},
  pages = {41--48},
  publisher = {ACM Press/Addison-Wesley Publishing Co.},
  series = {CHI '98},
  title = {Information archiving with bookmarks: personal Web space construction and organization},
  url = {http://dx.doi.org/10.1145/274644.274651},
  year = 1998
}

@inproceedings{dominguezgarcia2012freset,
  abstract = {FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.},
  acmid = {2365939},
  address = {New York, NY, USA},
  author = {Dom\'{\i}nguez Garc\'{\i}a, Renato and Bender, Matthias and Anjorin, Mojisola and Rensing, Christoph and Steinmetz, Ralf},
  booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web},
  doi = {10.1145/2365934.2365939},
  interhash = {489207308b5d7f064163652763794ce6},
  intrahash = {c78b033eb1b463ff00c4fc67ed8bf679},
  isbn = {978-1-4503-1638-5},
  location = {Dublin, Ireland},
  numpages = {4},
  pages = {25--28},
  publisher = {ACM},
  series = {RSWeb '12},
  title = {FReSET: an evaluation framework for folksonomy-based recommender systems},
  url = {http://doi.acm.org/10.1145/2365934.2365939},
  year = 2012
}

@inproceedings{krause2008logsonomy,
  abstract = {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.},
  address = {New York, NY, USA},
  author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
  booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia},
  doi = {http://doi.acm.org/10.1145/1379092.1379123},
  interhash = {6d34ea1823d95b9dbf37d4db4d125d2a},
  intrahash = {76d81124951ae39060a8bc98f4883435},
  isbn = {978-1-59593-985-2},
  location = {Pittsburgh, PA, USA},
  pages = {157--166},
  publisher = {ACM},
  title = {Logsonomy - Social Information Retrieval with Logdata},
  url = {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},
  vgwort = {17},
  year = 2008
}

@inproceedings{krause2008comparison,
  abstract = {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.</p> <p>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.</p> <p>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.},
  acmid = {1793290},
  address = {Berlin, Heidelberg},
  author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the IR research, 30th European conference on Advances in information retrieval},
  interhash = {37598733b747093d97a0840a11beebf5},
  intrahash = {039ff6ddae0794aceb5ccaecb88e3cb6},
  isbn = {3-540-78645-7, 978-3-540-78645-0},
  location = {Glasgow, UK},
  numpages = {13},
  pages = {101--113},
  publisher = {Springer-Verlag},
  series = {ECIR'08},
  title = {A comparison of social bookmarking with traditional search},
  url = {http://dl.acm.org/citation.cfm?id=1793274.1793290},
  year = 2008
}