http://www.bibsonomy.org/burst/user/hotho/queryBibSonomy BuRST Feed for /user/hotho/query2008-07-26T21:21:19+02:00http://www.bibsonomy.org/bibtex/2613f5c41ff759fc548c9085102d1c933/hothohotho2008-05-19T14:43:47+02:00social query search myown 2008 log bookmarking <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008, </em><em>4956, </em><em>page101-113. </em><em>Springer, </em>(<em>2008</em>)Mon May 19 14:43:47 CEST 2008Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008101-113A Comparison of Social Bookmarking with Traditional Search49562008social query search myown 2008 log bookmarking 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.http://www.bibsonomy.org/bibtex/2359e1eccdc524334d4a2ad51330f76ae/hothohotho2008-04-05T17:54:56+02:00icwsm folksonomy 2008 logsonomy analysis network myown log query search <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008), </em><em>AAAI Press, </em>(<em>2008</em>)Sat Apr 05 17:54:56 CEST 2008Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)Logsonomy — A Search Engine Folksonomy2008icwsm folksonomy 2008 logsonomy analysis network myown log query search 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.A survey on the use of relevance feedback for information access systemshttp://www.bibsonomy.org/bibtex/20357d6b4d3aa885a0978036d50136373/hothohotho2008-01-29T08:47:47+01:00ir relevance query feedback surevy expansion <span style="color:#555555;">Ian <a href="http://www.bibsonomy.org/author/Ruthven">Ruthven</a> and Mounia <a href="http://www.bibsonomy.org/author/Lalmas">Lalmas</a> </span><em>Knowl. Eng. Rev.</em><em>18(2):95--145</em>(<em>2003</em>)Tue Jan 29 08:47:47 CET 2008New York, NY, USAKnowl. Eng. Rev.295--145A survey on the use of relevance feedback for information access systems182003ir relevance query feedback surevy expansion Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems.Wiley InterScience: Journal: Abstracthttp://www.bibsonomy.org/bibtex/296cc916a85ed034ea7fa72e4259b9f05/hothohotho2008-01-29T08:25:39+01:00query expansion similarity ir term <span style="color:#555555;">Helen J. <a href="http://www.bibsonomy.org/author/Peat">Peat</a> and Peter <a href="http://www.bibsonomy.org/author/Willett">Willett</a> </span><em>Journal of the American Society for Information Science</em><em>42(5):378-383</em>(<em>1991</em>)Tue Jan 29 08:25:39 CET 2008Department of Information Studies, University of Sheffield, Western Bank, Sheffield S10 2TN, United KingdomJournal of the American Society for Information Science5378-383The limitations of term co-occurrence data for query expansion in document retrieval systems421991query expansion similarity ir term BibTex Formathttp://www.bibsonomy.org/bibtex/28609bcd383c3f14bf4aac32dae8e970b/hothohotho2006-04-23T17:18:22+02:00ir query expansion <span style="color:#555555;">Bruno M. <a href="http://www.bibsonomy.org/author/Fonseca">Fonseca</a> and Paulo <a href="http://www.bibsonomy.org/author/Golgher">Golgher</a> and Bruno <a href="http://www.bibsonomy.org/author/Pôssas">P&#244;ssas</a> and Berthier <a href="http://www.bibsonomy.org/author/Ribeiro-Neto">Ribeiro-Neto</a> and Nivio <a href="http://www.bibsonomy.org/author/Ziviani">Ziviani</a> </span><em>CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, </em><em>page696--703. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2005</em>)Sun Apr 23 17:18:22 CEST 2006New York, NY, USACIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management696--703Concept-based interactive query expansion2005ir query expansion