@inproceedings{Jaeschke2008logsonomy,
  abstract = {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.},
  author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)},
  interhash = {13ec3f45fc7e0364cdc6b9a7c12c5c2c},
  intrahash = {359e1eccdc524334d4a2ad51330f76ae},
  publisher = {AAAI Press},
  title = {Logsonomy — A Search Engine Folksonomy},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf},
  year = 2008
}

@inproceedings{1135858,
  abstract = {It has become a promising direction to measure similarity of Web search queries by mining the increasing amount of click-through data logged by Web search engines, which record the interactions between users and the search engines. Most existing approaches employ the click-through data for similarity measure of queries with little consideration of the temporal factor, while the click-through data is often dynamic and contains rich temporal information. In this paper we present a new framework of time-dependent query semantic similarity model on exploiting the temporal characteristics of historical click-through data. The intuition is that more accurate semantic similarity values between queries can be obtained by taking into account the timestamps of the log data. With a set of user-defined calendar schema and calendar patterns, our time-dependent query similarity model is constructed using the marginalized kernel technique, which can exploit both explicit similarity and implicit semantics from the click-through data effectively. Experimental results on a large set of click-through data acquired from a commercial search engine show that our time-dependent query similarity model is more accurate than the existing approaches. Moreover, we observe that our time-dependent query similarity model can, to some extent, reflect real-world semantics such as real-world events that are happening over time.},
  address = {New York, NY, USA},
  author = {Zhao, Qiankun and Hoi, Steven C. H. and Liu, Tie-Yan and Bhowmick, Sourav S. and Lyu, Michael R. and Ma, Wei-Ying},
  booktitle = {WWW '06: Proceedings of the 15th international conference on World Wide Web},
  doi = {http://doi.acm.org/10.1145/1135777.1135858},
  interhash = {c765e101c37f6b530e2c1c59808048d7},
  intrahash = {57cbc64550d3a1b5b8599a0783e95111},
  isbn = {1-59593-323-9},
  location = {Edinburgh, Scotland},
  pages = {543--552},
  publisher = {ACM Press},
  title = {Time-dependent semantic similarity measure of queries using historical click-through data},
  url = {http://portal.acm.org/citation.cfm?id=1135858},
  year = 2006
}

@inproceedings{1281204,
  address = {New York, NY, USA},
  author = {Baeza-Yates, Ricardo and Tiberi, Alessandro},
  booktitle = {KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining},
  doi = {http://doi.acm.org/10.1145/1281192.1281204},
  interhash = {26ca034be705abaf072835784f53d877},
  intrahash = {6e45b65feffd1545c6dca62bf4b8f53d},
  isbn = {978-1-59593-609-7},
  location = {San Jose, California, USA},
  pages = {76--85},
  publisher = {ACM},
  title = {Extracting semantic relations from query logs},
  url = {http://portal.acm.org/citation.cfm?id=1281204},
  year = 2007
}

@inproceedings{4597173,
  abstract = {One challenge for relevance ranking in Web search is underspecified queries. For such queries, top-ranked documents may contain information irrelevant to the search goal of the user; some newly-created relevant documents are ranked lower due to their freshness and to the large number of existing documents that match the queries. To improve the relevance ranking for underspecified queries requires better understanding of users' search goals. By analyzing the semantic query context extracted from the query logs, we propose Q-Rank to effectively improve the ranking of search results for a given query. Experiments show that Q-Rank outperforms the current ranking system of a large-scale commercial Web search engine, improving the relevance ranking for 82% of the queries with an average increase of 8.99% in terms of discounted cumulative gains. Because Q-Rank is independent of the underlying ranking algorithm, it can be integrated with existing search engines.},
  author = {Zhuang, Z. and Cucerzan, S.},
  doi = {10.1109/ICSC.2008.8},
  interhash = {fd70fee1920ea227a8c336fe80e2ba71},
  intrahash = {8c2005e1dea667cdd23a8e5c7efe9243},
  journal = {Semantic Computing, 2008 IEEE International Conference on},
  month = {Aug.},
  pages = {50-57},
  title = {Exploiting Semantic Query Context to Improve Search Ranking},
  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. 
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.},
  author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008},
  interhash = {37598733b747093d97a0840a11beebf5},
  intrahash = {613f5c41ff759fc548c9085102d1c933},
  pages = {101-113},
  publisher = {Springer},
  title = {A Comparison of Social Bookmarking  with Traditional Search},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/ecir2008krause.pdf},
  volume = 4956,
  year = 2008
}

@inproceedings{doerfel2014social,
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  booktitle = {Proceedings of the 23rd International World Wide Web Conference},
  interhash = {9223d6d728612c8c05a80b5edceeb78b},
  intrahash = {11fab5468dd4b4e3db662ea5e68df8e0},
  publisher = {ACM},
  series = {WWW 2014},
  title = {How Social is Social Tagging?},
  year = 2014
}