PUMA publications for /user/stephandoerfel/toreadhttps://puma.uni-kassel.de/user/stephandoerfel/toreadPUMA RSS feed for /user/stephandoerfel/toread2024-03-19T07:29:26+01:00“Supertagger” Behavior in Building Folksonomieshttps://puma.uni-kassel.de/bibtex/2014abc7dc30e38859c5e8605dce1a8f6/stephandoerfelstephandoerfel2014-06-26T10:35:52+02:00analysis distribution folksonomy supertagger tag tagging toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jared Lorince" itemprop="url" href="/author/Jared%20Lorince"><span itemprop="name">J. Lorince</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sam Zorowitz" itemprop="url" href="/author/Sam%20Zorowitz"><span itemprop="name">S. Zorowitz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jaimie Murdock" itemprop="url" href="/author/Jaimie%20Murdock"><span itemprop="name">J. Murdock</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peter Todd" itemprop="url" href="/author/Peter%20Todd"><span itemprop="name">P. Todd</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"></span>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Thu Jun 26 10:35:52 CEST 2014“Supertagger” Behavior in Building Folksonomies2014analysis distribution folksonomy supertagger tag tagging toRead Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systemshttps://puma.uni-kassel.de/bibtex/2d1074484ea350ad88400fe4fc6984874/stephandoerfelstephandoerfel2014-01-08T13:17:51+01:00bibsonomy folksonomy information management motivation social tagging toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus Heckner" itemprop="url" href="/author/Markus%20Heckner"><span itemprop="name">M. Heckner</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael Heilemann" itemprop="url" href="/author/Michael%20Heilemann"><span itemprop="name">M. Heilemann</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Wolff" itemprop="url" href="/author/Christian%20Wolff"><span itemprop="name">C. Wolff</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Int'l AAAI Conference on Weblogs and Social Media (ICWSM)</span>, </em></span><em>San Jose, CA, USA, </em>(<em><span>Mai 2009<meta content="Mai 2009" itemprop="datePublished"/></span></em>)Wed Jan 08 13:17:51 CET 2014San Jose, CA, USAInt'l AAAI Conference on Weblogs and Social Media (ICWSM)mayPersonal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems2009bibsonomy folksonomy information management motivation social tagging toread Research Paper Recommender System Evaluation: A Quantitative Literature Surveyhttps://puma.uni-kassel.de/bibtex/24afa2bd342dda6b6d32713aa0fbc33bd/stephandoerfelstephandoerfel2013-10-01T11:23:45+02:00evaluation paper recommender research toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joeran Beel" itemprop="url" href="/author/Joeran%20Beel"><span itemprop="name">J. Beel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Langer" itemprop="url" href="/author/Stefan%20Langer"><span itemprop="name">S. Langer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marcel Genzmehr" itemprop="url" href="/author/Marcel%20Genzmehr"><span itemprop="name">M. Genzmehr</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bela Gipp" itemprop="url" href="/author/Bela%20Gipp"><span itemprop="name">B. Gipp</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Corinna Breitinger" itemprop="url" href="/author/Corinna%20Breitinger"><span itemprop="name">C. Breitinger</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Nürnberger" itemprop="url" href="/author/Andreas%20N%c3%bcrnberger"><span itemprop="name">A. Nürnberger</span></a></span>. </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Tue Oct 01 11:23:45 CEST 2013Research Paper Recommender System Evaluation: A Quantitative Literature Survey2013evaluation paper recommender research toread Broken Promises of Privacy: Responding to the Surprising Failure of Anonymizationhttps://puma.uni-kassel.de/bibtex/261225f84d3fc809981d436ffe76489b6/stephandoerfelstephandoerfel2012-08-06T13:30:33+02:00anonymity info20 privacy toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paul Ohm" itemprop="url" href="/author/Paul%20Ohm"><span itemprop="name">P. Ohm</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>UCLA Law Review, Vol. 57, p. 1701, 2010</em></span></span> </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Mon Aug 06 13:30:33 CEST 2012UCLA Law Review, Vol. 57, p. 1701, 2010Broken Promises of Privacy: Responding to the Surprising Failure of AnonymizationAccepted Paper Series2009anonymity info20 privacy toRead Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization by Paul Ohm :: SSRNPersonality and motivations associated with Facebook usehttps://puma.uni-kassel.de/bibtex/2fbcbb77a298da03d1e2d8bcd9ac3e0b4/stephandoerfelstephandoerfel2012-03-13T09:12:35+01:00facebook network sna social sociology toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Craig Ross" itemprop="url" href="/author/Craig%20Ross"><span itemprop="name">C. Ross</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Emily S. Orr" itemprop="url" href="/author/Emily%20S.%20Orr"><span itemprop="name">E. Orr</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mia Sisic" itemprop="url" href="/author/Mia%20Sisic"><span itemprop="name">M. Sisic</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jaime M. Arseneault" itemprop="url" href="/author/Jaime%20M.%20Arseneault"><span itemprop="name">J. Arseneault</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mary G. Simmering" itemprop="url" href="/author/Mary%20G.%20Simmering"><span itemprop="name">M. Simmering</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Robert Orr" itemprop="url" href="/author/R.%20Robert%20Orr"><span itemprop="name">R. Orr</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Computers in Human Behavior</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">25 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">578 - 586</span></em> </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)<em><ce:title>Including the Special Issue: State of the Art Research into Cognitive Load Theory</ce:title>.</em>Tue Mar 13 09:12:35 CET 2012Computers in Human Behavior<ce:title>Including the Special Issue: State of the Art Research into Cognitive Load Theory</ce:title>2578 - 586Personality and motivations associated with Facebook use252009facebook network sna social sociology toRead Facebook is quickly becoming one of the most popular tools for social communication. However, Facebook is somewhat different from other Social Networking Sites as it demonstrates an offline-to-online trend; that is, the majority of Facebook Friends are met offline and then added later. The present research investigated how the Five-Factor Model of personality relates to Facebook use. Despite some expected trends regarding Extraversion and Openness to Experience, results indicated that personality factors were not as influential as previous literature would suggest. The results also indicated that a motivation to communicate was influential in terms of Facebook use. It is suggested that different motivations may be influential in the decision to use tools such as Facebook, especially when individual functions of Facebook are being considered.Personality and motivations associated with Facebook use 10.1016/j.chb.2008.12.024 : Computers in Human Behavior | ScienceDirect.comDon't look stupid: avoiding pitfalls when recommending research papershttps://puma.uni-kassel.de/bibtex/27775150ca225770019bd94db9be5db40/stephandoerfelstephandoerfel2012-03-09T14:02:25+01:00itemRecommendation paper pitfalls recommender stupid toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sean M. McNee" itemprop="url" href="/author/Sean%20M.%20McNee"><span itemprop="name">S. McNee</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nishikant Kapoor" itemprop="url" href="/author/Nishikant%20Kapoor"><span itemprop="name">N. Kapoor</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joseph A. Konstan" itemprop="url" href="/author/Joseph%20A.%20Konstan"><span itemprop="name">J. Konstan</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work</span>, </em></span><em>Seite <span itemprop="pagination">171--180</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Fri Mar 09 14:02:25 CET 2012New York, NY, USAProceedings of the 2006 20th anniversary conference on Computer supported cooperative work171--180CSCW '06Don't look stupid: avoiding pitfalls when recommending research papers2006itemRecommendation paper pitfalls recommender stupid toRead If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.Don't look stupidRandomization techniques for graphshttps://puma.uni-kassel.de/bibtex/2a48be9257b9ca84ad9a426d4ff55d309/stephandoerfelstephandoerfel2012-03-06T08:37:27+01:00graphs randomization toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sami Hanhijärvi" itemprop="url" href="/author/Sami%20Hanhij%c3%a4rvi"><span itemprop="name">S. Hanhijärvi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gemma Garriga" itemprop="url" href="/author/Gemma%20Garriga"><span itemprop="name">G. Garriga</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kai Puolamäki" itemprop="url" href="/author/Kai%20Puolam%c3%a4ki"><span itemprop="name">K. Puolamäki</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"> </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Tue Mar 06 08:37:27 CET 2012Randomization techniques for graphs2009graphs randomization toRead Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those results is lacking. Within the framework of statistical hypothesis testing, we focus in this paper on randomization techniques for unweighted undirected graphs. Randomization is an important approach to assess the statistical significance of data mining results. Given an input graph, our randomization method will sample data from the class of graphs that share certain structural properties with the input graph. Here we describe three alternative algorithms based on local edge swapping and Metropolis sampling. We test our framework with various graph data sets and mining algorithms for two applications, namely graph clustering and frequent subgraph mining.Scientific Commons: Randomization techniques for graphs (2009), 2009 [Hanhijärvi, Sami, Garriga, Gemma, Puolamäki, Kai]Factorizing personalized Markov chains for next-basket recommendationhttps://puma.uni-kassel.de/bibtex/2a806788d8f8f9259624a853551e40c30/stephandoerfelstephandoerfel2011-07-15T00:43:29+02:00toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Rendle" itemprop="url" href="/author/Steffen%20Rendle"><span itemprop="name">S. Rendle</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Freudenthaler" itemprop="url" href="/author/Christoph%20Freudenthaler"><span itemprop="name">C. Freudenthaler</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Schmidt-Thieme Lars" itemprop="url" href="/author/Schmidt-Thieme%20Lars"><span itemprop="name">S. Lars</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 19th international conference on World wide web</span>, </em></span><em>Seite <span itemprop="pagination">811--820</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Fri Jul 15 00:43:29 CEST 2011New York, NY, USAProceedings of the 19th international conference on World wide web811--820WWW '10Factorizing personalized Markov chains for next-basket recommendation2010toRead Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned - thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.Factorizing personalized Markov chains for next-basket recommendationCollaborative filtering with decoupled models for preferences and ratingshttps://puma.uni-kassel.de/bibtex/21da2fda40d4a93cb43c7f2f058c0cd3f/stephandoerfelstephandoerfel2011-05-11T10:01:56+02:00collaborative info20 rating toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rong Jin" itemprop="url" href="/author/Rong%20Jin"><span itemprop="name">R. Jin</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Luo Si" itemprop="url" href="/author/Luo%20Si"><span itemprop="name">L. Si</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="ChengXiang Zhai" itemprop="url" href="/author/ChengXiang%20Zhai"><span itemprop="name">C. Zhai</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jamie Callan" itemprop="url" href="/author/Jamie%20Callan"><span itemprop="name">J. Callan</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the twelfth international conference on Information and knowledge management</span>, </em></span><em>Seite <span itemprop="pagination">309--316</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Wed May 11 10:01:56 CEST 2011New York, NY, USAProceedings of the twelfth international conference on Information and knowledge management309--316CIKM '03Collaborative filtering with decoupled models for preferences and ratings2003collaborative info20 rating toRead In this paper, we describe a new model for collaborative filtering. The motivation of this work comes from the fact that two users with very similar preferences on items may have very different rating schemes. For example, one user may tend to assign a higher rating to all items than another user. Unlike previous models of collaborative filtering, which determine the similarity between two users only based on their rating performance, our model treats the user's preferences on items separately from the user's rating scheme. More specifically, for each user, we build two separate models: a preference model capturing which items are favored by the user and a rating model capturing how the user would rate an item given the preference information. The similarity of two users is computed based on the underlying preference model, instead of the surface ratings. We compare the new model with several representative previous approaches on two data sets. Experiment results show that the new model outperforms all the previous approaches that are tested consistently on both data sets.Collaborative filtering with decoupled models for preferences and ratingsMining and summarizing customer reviewshttps://puma.uni-kassel.de/bibtex/288f1001cb2a901275383ad63b87d61fc/stephandoerfelstephandoerfel2011-02-01T13:40:26+01:00customer info2.0 mining review reviews summarizing toRead wordmining <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Minqing Hu" itemprop="url" href="/author/Minqing%20Hu"><span itemprop="name">M. Hu</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bing Liu" itemprop="url" href="/author/Bing%20Liu"><span itemprop="name">B. Liu</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining</span>, </em></span><em>Seite <span itemprop="pagination">168--177</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Tue Feb 01 13:40:26 CET 2011New York, NY, USAProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining168--177KDD '04Mining and summarizing customer reviews2004customer info2.0 mining review reviews summarizing toRead wordmining Mining and summarizing customer reviewsIndex design and query processing for graph conductance searchhttps://puma.uni-kassel.de/bibtex/2dcc951cd461fe1c454db7a738429d421/stephandoerfelstephandoerfel2011-01-24T16:02:50+01:00design graph index interactive pagerank processing query toRead folkrank <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Soumen Chakrabarti" itemprop="url" href="/author/Soumen%20Chakrabarti"><span itemprop="name">S. Chakrabarti</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Amit Pathak" itemprop="url" href="/author/Amit%20Pathak"><span itemprop="name">A. Pathak</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Manish Gupta" itemprop="url" href="/author/Manish%20Gupta"><span itemprop="name">M. Gupta</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>The VLDB Journal</em></span></span> </span>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Mon Jan 24 16:02:50 CET 2011Berlin / HeidelbergThe VLDB Journal1-26Index design and query processing for graph conductance search2010design graph index interactive pagerank processing query toRead folkrank Graph conductance queries, also known as personalized PageRank and related to random walks with restarts, were originally proposed to assign a hyperlink-based prestige score to Web pages. More general forms of such queries are also very useful for ranking in entity-relation (ER) graphs used to represent relational, XML and hypertext data. Evaluation of PageRank usually involves a global eigen computation. If the graph is even moderately large, interactive response times may not be possible. Recently, the need for interactive PageRank evaluation has increased. The graph may be fully known only when the query is submitted. Browsing actions of the user may change some inputs to the PageRank computation dynamically. In this paper, we describe a system that analyzes query workloads and the ER graph, invests in limited offline indexing, and exploits those indices to achieve essentially constant-time query processing, even as the graph size scales. Our techniques—data and query statistics collection, index selection and materialization, and query-time index exploitation—have parallels in the extensive relational query optimization literature, but is applied to supporting novel graph data repositories. We report on experiments with five temporal snapshots of the CiteSeer ER graph having 74–702 thousand entity nodes, 0.17–1.16 million word nodes, 0.29–3.26 million edges between entities, and 3.29–32.8 million edges between words and entities. We also used two million actual queries from CiteSeer’s logs. Queries run 3–4 orders of magnitude faster than whole-graph PageRank, the gap growing with graph size. Index size is smaller than a text index. Ranking accuracy is 94–98% with reference to whole-graph PageRank.SpringerLink - The VLDB Journal, Online First™A fuzzy model for reasoning about reputation in web serviceshttps://puma.uni-kassel.de/bibtex/2afa80aad5c9222e20177793dfae5945a/stephandoerfelstephandoerfel2011-01-17T09:35:50+01:00info2.0 rating reputation review toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wanita Sherchan" itemprop="url" href="/author/Wanita%20Sherchan"><span itemprop="name">W. Sherchan</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Seng W. Loke" itemprop="url" href="/author/Seng%20W.%20Loke"><span itemprop="name">S. Loke</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shonali Krishnaswamy" itemprop="url" href="/author/Shonali%20Krishnaswamy"><span itemprop="name">S. Krishnaswamy</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2006 ACM symposium on Applied computing</span>, </em></span><em>Seite <span itemprop="pagination">1886--1892</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Mon Jan 17 09:35:50 CET 2011New York, NY, USAProceedings of the 2006 ACM symposium on Applied computing1886--1892SAC '06A fuzzy model for reasoning about reputation in web services2006info2.0 rating reputation review toread A fuzzy model for reasoning about reputation in web servicesRelevance Ranking using Kernelshttps://puma.uni-kassel.de/bibtex/24d086714a580d80c68077fcc98656db3/stephandoerfelstephandoerfel2010-11-24T17:08:25+01:00algorithm folksonomy kernel kernels ranking relevance toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jun Xu" itemprop="url" href="/author/Jun%20Xu"><span itemprop="name">J. Xu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hang Li" itemprop="url" href="/author/Hang%20Li"><span itemprop="name">H. Li</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chaoliang Zhong" itemprop="url" href="/author/Chaoliang%20Zhong"><span itemprop="name">C. Zhong</span></a></span>. </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Wed Nov 24 17:08:25 CET 2010Relevance Ranking using Kernels2009algorithm folksonomy kernel kernels ranking relevance toRead A Fast Effective Multi-Channeled Tag Recommenderhttps://puma.uni-kassel.de/bibtex/2cea0f6c4149738eb084852aa6c71b935/stephandoerfelstephandoerfel2010-11-19T15:33:19+01:00toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jonathan Gemmell" itemprop="url" href="/author/Jonathan%20Gemmell"><span itemprop="name">J. Gemmell</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maryam Ramezani" itemprop="url" href="/author/Maryam%20Ramezani"><span itemprop="name">M. Ramezani</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Schimoler" itemprop="url" href="/author/Thomas%20Schimoler"><span itemprop="name">T. Schimoler</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Laura Christiansen" itemprop="url" href="/author/Laura%20Christiansen"><span itemprop="name">L. Christiansen</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">ECML PKDD Discovery Challenge 2009 (DC09)</span>, </em></span><em> 497, </em><em>Bled, Slovenia, </em><em><span itemprop="publisher">CEUR Workshop Proceedings</span>, </em>(<em><span>September 2009<meta content="September 2009" itemprop="datePublished"/></span></em>)Fri Nov 19 15:33:19 CET 2010Bled, SloveniaECML PKDD Discovery Challenge 2009 (DC09)SeptemberA Fast Effective Multi-Channeled Tag Recommender4972009toRead Collaborative tagging applications allow users to annotate online resources, resulting in a complex three dimensional network of interrelated users, resources and tags often called a folksonom A pivotal challenge of these systems remains the inclusion of the varied information channels introduced by the multi-dimensional folksonomy into recommendation techniques. In this paper we propose a composite tag recommender based upon popularity and collaborative filtering. These recommenders were chosen based on their speed, memory requirements and ability to cover complimentary channels of the folksonomy. Alone these recommenders perform poorly; together they achieve a synergy which proves to be as effective as state of the art tag recommenders.The impact of ambiguity and redundancy on tag recommendation in folksonomieshttps://puma.uni-kassel.de/bibtex/20710acde8c3db11f5dbd63f76bd30dc6/stephandoerfelstephandoerfel2010-11-19T15:33:01+01:00toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jonathan Gemmell" itemprop="url" href="/author/Jonathan%20Gemmell"><span itemprop="name">J. Gemmell</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maryam Ramezani" itemprop="url" href="/author/Maryam%20Ramezani"><span itemprop="name">M. Ramezani</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Schimoler" itemprop="url" href="/author/Thomas%20Schimoler"><span itemprop="name">T. Schimoler</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Laura Christiansen" itemprop="url" href="/author/Laura%20Christiansen"><span itemprop="name">L. Christiansen</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">RecSys '09: Proceedings of the third ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">45--52</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Fri Nov 19 15:33:01 CET 2010New York, NY, USARecSys '09: Proceedings of the third ACM conference on Recommender systems45--52The impact of ambiguity and redundancy on tag recommendation in folksonomies2009toRead Collaborative tagging applications have become a popular tool allowing Internet users to manage online resources with tags. Most collaborative tagging applications permit unsupervised tagging resulting in tag ambiguity in which a single tag has many different meanings and tag redundancy in which several tags have the same meaning. Common metrics for evaluating tag recommenders may overestimate the utility of ambiguous tags or ignore the appropriateness of redundant tags. Ambiguity and redundancy may even burden the user with additional effort by requiring them to clarify an annotation or forcing them to distinguish between highly related items. In this paper we demonstrate that ambiguity and redundancy impede the evaluation and performance of tag recommenders. Five tag recommendation strategies based on popularity, collaborative filtering and link analysis are explored. We use a cluster-based approach to define ambiguity and redundancy and provide extensive evaluation on three real world datasets.The impact of ambiguity and redundancy on tag recommendation in folksonomiesAdapting K-Nearest Neighbor for Tag Recommendation in Folksonomies.https://puma.uni-kassel.de/bibtex/28b50a08149b62c6fed95fd6e557f89bf/stephandoerfelstephandoerfel2010-11-19T15:32:47+01:00toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jonathan Gemmell" itemprop="url" href="/author/Jonathan%20Gemmell"><span itemprop="name">J. Gemmell</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Schimoler" itemprop="url" href="/author/Thomas%20Schimoler"><span itemprop="name">T. Schimoler</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maryam Ramezani" itemprop="url" href="/author/Maryam%20Ramezani"><span itemprop="name">M. Ramezani</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">ITWP</span>, </em></span><em>Volume 528 von CEUR Workshop Proceedings, </em><em><span itemprop="publisher">CEUR-WS.org</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Fri Nov 19 15:32:47 CET 2010ITWPconf/ijcai/2009itwpCEUR Workshop ProceedingsAdapting K-Nearest Neighbor for Tag Recommendation in Folksonomies.5282009toRead dblpMatrix algebrahttps://puma.uni-kassel.de/bibtex/2fccc8b26fcc1912304600c6410f241e5/stephandoerfelstephandoerfel2010-07-20T12:12:54+02:00algebra endnote matrix toRead <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="James E. Gentle" itemprop="url" href="/author/James%20E.%20Gentle"><span itemprop="name">J. Gentle</span></a></span>. </span><em><span itemprop="publisher">Springer New York</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Tue Jul 20 12:12:54 CEST 2010Matrix algebra2007algebra endnote matrix toRead Bibliogr. S. [505] - 518UB Kassel