PUMA publications for /author/Sunhttps://puma.uni-kassel.de/author/SunPUMA RSS feed for /author/Sun2024-03-28T17:18:34+01:00Social Dynamics of Sciencehttps://puma.uni-kassel.de/bibtex/2ed28353b082f3ccbd23ea85ea9d7c8e5/stephandoerfelstephandoerfel2015-03-20T10:58:13+01:00dynamics social bibsonomy <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiaoling Sun" itemprop="url" href="/author/Xiaoling%20Sun"><span itemprop="name">X. Sun</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jasleen Kaur" itemprop="url" href="/author/Jasleen%20Kaur"><span itemprop="name">J. Kaur</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stasa Milojevic" itemprop="url" href="/author/Stasa%20Milojevic"><span itemprop="name">S. Milojevic</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alessandro Flammini" itemprop="url" href="/author/Alessandro%20Flammini"><span itemprop="name">A. Flammini</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Filippo Menczer" itemprop="url" href="/author/Filippo%20Menczer"><span itemprop="name">F. Menczer</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>Sci. Rep.</em></span></span> </span>(<em><span>Januar 2013<meta content="Januar 2013" itemprop="datePublished"/></span></em>)Fri Mar 20 10:58:13 CET 2015Sci. Rep.janSocial Dynamics of Science32013dynamics social bibsonomy Social Dynamics of Science : Scientific Reports : Nature Publishing GroupSocial Dynamics of Sciencehttps://puma.uni-kassel.de/bibtex/2721dcd5644cca27fd50d8e6ffd667056/jaeschkejaeschke2013-02-19T17:55:58+01:00dynamics scientometrics science social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiaoling Sun" itemprop="url" href="/author/Xiaoling%20Sun"><span itemprop="name">X. Sun</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jasleen Kaur" itemprop="url" href="/author/Jasleen%20Kaur"><span itemprop="name">J. Kaur</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stasa Milojevic" itemprop="url" href="/author/Stasa%20Milojevic"><span itemprop="name">S. Milojevic</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alessandro Flammini" itemprop="url" href="/author/Alessandro%20Flammini"><span itemprop="name">A. Flammini</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Filippo Menczer" itemprop="url" href="/author/Filippo%20Menczer"><span itemprop="name">F. Menczer</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>Scientific Reports</em></span></span> </span>(<em><span>Januar 2013<meta content="Januar 2013" itemprop="datePublished"/></span></em>)Tue Feb 19 17:55:58 CET 2013Scientific ReportsjanSocial Dynamics of Science32013dynamics scientometrics science social The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.Social Dynamics of Sciencehttps://puma.uni-kassel.de/bibtex/2ed28353b082f3ccbd23ea85ea9d7c8e5/hothohotho2013-02-19T17:52:06+01:00dynamics science social bibsonomy toread web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiaoling Sun" itemprop="url" href="/author/Xiaoling%20Sun"><span itemprop="name">X. Sun</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jasleen Kaur" itemprop="url" href="/author/Jasleen%20Kaur"><span itemprop="name">J. Kaur</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stasa Milojevic" itemprop="url" href="/author/Stasa%20Milojevic"><span itemprop="name">S. Milojevic</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alessandro Flammini" itemprop="url" href="/author/Alessandro%20Flammini"><span itemprop="name">A. Flammini</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Filippo Menczer" itemprop="url" href="/author/Filippo%20Menczer"><span itemprop="name">F. Menczer</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>Sci. Rep.</em></span></span> </span>(<em><span>Januar 2013<meta content="Januar 2013" itemprop="datePublished"/></span></em>)Tue Feb 19 17:52:06 CET 2013Sci. Rep.janSocial Dynamics of Science32013dynamics science social bibsonomy toread web Social Dynamics of Science : Scientific Reports : Nature Publishing GroupCombining content and relation analysis for recommendation in social tagging systemshttps://puma.uni-kassel.de/bibtex/284f824839090a5e20394b85a9e1cef08/stephandoerfelstephandoerfel2012-11-16T12:08:37+01:00combining relation analysis recommendation content <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yin Zhang" itemprop="url" href="/author/Yin%20Zhang"><span itemprop="name">Y. Zhang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bin Zhang" itemprop="url" href="/author/Bin%20Zhang"><span itemprop="name">B. Zhang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kening Gao" itemprop="url" href="/author/Kening%20Gao"><span itemprop="name">K. Gao</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pengwei Guo" itemprop="url" href="/author/Pengwei%20Guo"><span itemprop="name">P. Guo</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daming Sun" itemprop="url" href="/author/Daming%20Sun"><span itemprop="name">D. Sun</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>Physica A: Statistical Mechanics and its Applications</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">391 </span></span>(<span itemprop="issueNumber">22</span>):
<span itemprop="pagination">5759 - 5768</span></em> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Fri Nov 16 12:08:37 CET 2012Physica A: Statistical Mechanics and its Applications225759 - 5768Combining content and relation analysis for recommendation in social tagging systems3912012combining relation analysis recommendation content Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.ScienceDirect.com - Physica A: Statistical Mechanics and its Applications - Combining content and relation analysis for recommendation in social tagging systemsCombining content and relation analysis for recommendation in social tagging systemshttps://puma.uni-kassel.de/bibtex/284f824839090a5e20394b85a9e1cef08/hothohotho2012-11-16T11:53:59+01:00model lda ranking folksonomy folkrank toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yin Zhang" itemprop="url" href="/author/Yin%20Zhang"><span itemprop="name">Y. Zhang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bin Zhang" itemprop="url" href="/author/Bin%20Zhang"><span itemprop="name">B. Zhang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kening Gao" itemprop="url" href="/author/Kening%20Gao"><span itemprop="name">K. Gao</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pengwei Guo" itemprop="url" href="/author/Pengwei%20Guo"><span itemprop="name">P. Guo</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daming Sun" itemprop="url" href="/author/Daming%20Sun"><span itemprop="name">D. Sun</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>Physica A: Statistical Mechanics and its Applications</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">391 </span></span>(<span itemprop="issueNumber">22</span>):
<span itemprop="pagination">5759 - 5768</span></em> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Fri Nov 16 11:53:59 CET 2012Physica A: Statistical Mechanics and its Applications225759 - 5768Combining content and relation analysis for recommendation in social tagging systems3912012model lda ranking folksonomy folkrank toread Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.ScienceDirect.com - Physica A: Statistical Mechanics and its Applications - Combining content and relation analysis for recommendation in social tagging systemsMeasuring article quality in wikipedia: models and evaluationhttps://puma.uni-kassel.de/bibtex/2cd9077443f7519e9cdce492858753632/jaeschkejaeschke2011-05-25T17:52:32+02:00document quality model wikipedia <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Meiqun Hu" itemprop="url" href="/author/Meiqun%20Hu"><span itemprop="name">M. Hu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ee-Peng Lim" itemprop="url" href="/author/Ee-Peng%20Lim"><span itemprop="name">E. Lim</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Aixin Sun" itemprop="url" href="/author/Aixin%20Sun"><span itemprop="name">A. Sun</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hady Wirawan Lauw" itemprop="url" href="/author/Hady%20Wirawan%20Lauw"><span itemprop="name">H. Lauw</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ba-Quy Vuong" itemprop="url" href="/author/Ba-Quy%20Vuong"><span itemprop="name">B. Vuong</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management</span>, </em></span><em>Seite <span itemprop="pagination">243--252</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Wed May 25 17:52:32 CEST 2011New York, NY, USAProceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management243--252CIKM '07Measuring article quality in wikipedia: models and evaluation2007document quality model wikipedia Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our B<scp>asic</scp> model is designed based on the mutual dependency between article quality and their author authority. The P<scp>eer</scp>R<scp>eview</scp> model introduces the review behavior into measuring article quality. Finally, our P<scp>rob</scp>R<scp>eview</scp> models extend P<scp>eer</scp>R<scp>eview</scp> with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement.TagPlus: A Retrieval System using Synonym Tag in Folksonomyhttps://puma.uni-kassel.de/bibtex/24344c37b828436f882b45f0f750ce1c4/benzbenz2011-02-17T23:23:21+01:00ol_web2.0 synonyms information_retrieval folksonomy methods_synonyms <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sun-Sook Lee" itemprop="url" href="/author/Sun-Sook%20Lee"><span itemprop="name">S. Lee</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hwan-Seung Yong" itemprop="url" href="/author/Hwan-Seung%20Yong"><span itemprop="name">H. Yong</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering</span>, </em></span><em>Seite <span itemprop="pagination">294--298</span>. </em><em>Washington, DC, USA, </em><em><span itemprop="publisher">IEEE Computer Society</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Thu Feb 17 23:23:21 CET 2011Washington, DC, USAProceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering294--298MUE '07TagPlus: A Retrieval System using Synonym Tag in Folksonomy2007ol_web2.0 synonyms information_retrieval folksonomy methods_synonyms Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs, videos and other content. In ubiquitous computing environment, users access data through various kinds of mobile terminals. Therefore users want more accurate materials because of expensive communication cost or the useless results due to abuse of tags. In this paper, we first describe current limitation of tagging services. We then describe the system (TagPlus) we implemented to minimize ambiguity due to no synonym control. Finally, we give experimental results.TagPlusWeb taxonomy integration using support vector machineshttps://puma.uni-kassel.de/bibtex/2b683a9061fe344cfbcd62aa85e44d2c4/benzbenz2011-02-04T16:09:58+01:00studienarbeit taxonomy_integration classification support_vector_machines ontology_mapping semantic_web transductive_learning eventually_useful <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dell Zhang" itemprop="url" href="/author/Dell%20Zhang"><span itemprop="name">D. Zhang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wee Sun Lee" itemprop="url" href="/author/Wee%20Sun%20Lee"><span itemprop="name">W. Lee</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">WWW '04: Proceedings of the 13th international conference on World Wide Web</span>, </em></span><em>Seite <span itemprop="pagination">472--481</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM Press</span>, </em>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Fri Feb 04 16:09:58 CET 2011New York, NY, USAWWW '04: Proceedings of the 13th international conference on World Wide Web472--481Web taxonomy integration using support vector machines2004studienarbeit taxonomy_integration classification support_vector_machines ontology_mapping semantic_web transductive_learning eventually_useful We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose a method, Cluster Shrinkage (CS), to further enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.Disambiguating Tags in Blogshttps://puma.uni-kassel.de/bibtex/2de61eca3fd7a58891a451f98bfc361ed/benzbenz2011-02-04T16:09:39+01:00ol_web2.0 data_blogs disambiguation tag_sense_disambigution <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiance Si" itemprop="url" href="/author/Xiance%20Si"><span itemprop="name">X. Si</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maosong Sun" itemprop="url" href="/author/Maosong%20Sun"><span itemprop="name">M. Sun</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">TSD</span>, </em></span><em>Volume 5729 von Lecture Notes in Computer Science, </em><em>Seite <span itemprop="pagination">139--146</span>. </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Fri Feb 04 16:09:39 CET 2011TSD139--146Lecture Notes in Computer ScienceDisambiguating Tags in Blogs57292009ol_web2.0 data_blogs disambiguation tag_sense_disambigution Blog users enjoy tagging for better document organization, while ambiguity in tags leads to inaccuracy in tag-based applications, such as retrieval, visualization or trend discovery. The dynamic nature of tag meanings makes current word sense disambiguation(WSD) methods not applicable. In this paper, we propose an unsupervised method for disambiguating tags in blogs. We first cluster the tags by their context words using Spectral Clustering. Then we compare a tag with these clusters to find the most suitable meaning. We use Normalized Google Distance to measure word similarity, which can be computed by querying search engines, thus reflects the up-to-date meaning of words. No human labeling efforts or dictionary needed in our method. Evaluation using crawled blog data showed a promising micro average precision of 0.842.Global ranking by exploiting user clickshttps://puma.uni-kassel.de/bibtex/2ca66cc173e65ef7fe5b0cd9bfb8646aa/jaeschkejaeschke2010-08-05T09:16:39+02:00learning-to-rank click ranking search web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shihao Ji" itemprop="url" href="/author/Shihao%20Ji"><span itemprop="name">S. Ji</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ke Zhou" itemprop="url" href="/author/Ke%20Zhou"><span itemprop="name">K. Zhou</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ciya Liao" itemprop="url" href="/author/Ciya%20Liao"><span itemprop="name">C. Liao</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zhaohui Zheng" itemprop="url" href="/author/Zhaohui%20Zheng"><span itemprop="name">Z. Zheng</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gui-Rong Xue" itemprop="url" href="/author/Gui-Rong%20Xue"><span itemprop="name">G. Xue</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Olivier Chapelle" itemprop="url" href="/author/Olivier%20Chapelle"><span itemprop="name">O. Chapelle</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gordon Sun" itemprop="url" href="/author/Gordon%20Sun"><span itemprop="name">G. Sun</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hongyuan Zha" itemprop="url" href="/author/Hongyuan%20Zha"><span itemprop="name">H. Zha</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval</span>, </em></span><em>Seite <span itemprop="pagination">35--42</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>)Thu Aug 05 09:16:39 CEST 2010New York, NY, USASIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval35--42Global ranking by exploiting user clicks2009learning-to-rank click ranking search web It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this paper, we focus on extracting relevance information from one source of user interactions, i.e., user click data, which records the sequence of documents being clicked and not clicked in the result set during a user search session. We formulate the problem as a global ranking problem, emphasizing the importance of the sequential nature of user clicks, with the goal to predict the relevance labels of all the documents in a search session. This is distinct from conventional learning to rank methods that usually design a ranking model defined on a single document; in contrast, in our model the relational information among the documents as manifested by an aggregation of user clicks is exploited to rank all the documents jointly. In particular, we adapt several sequential supervised learning algorithms, including the conditional random field (CRF), the sliding window method and the recurrent sliding window method, to the global ranking problem. Experiments on the click data collected from a commercial search engine demonstrate that our methods can outperform the baseline models for search results re-ranking.Content-based and Graph-based Tag Suggestionhttps://puma.uni-kassel.de/bibtex/2de2233e0713a1cefbf5f5ccde074e31d/jaeschkejaeschke2009-12-16T11:42:31+01:00dc09 recommender tag <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Xiance Si" itemprop="url" href="/author/Xiance%20Si"><span itemprop="name">X. Si</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zhiyuan Liu" itemprop="url" href="/author/Zhiyuan%20Liu"><span itemprop="name">Z. Liu</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peng Li" itemprop="url" href="/author/Peng%20Li"><span itemprop="name">P. Li</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Qixia Jiang" itemprop="url" href="/author/Qixia%20Jiang"><span itemprop="name">Q. Jiang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maosong Sun" itemprop="url" href="/author/Maosong%20Sun"><span itemprop="name">M. Sun</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"></span><em>Volume 497 von CEUR-WS.org, </em><em>Seite <span itemprop="pagination">243--260</span>. </em>(<em><span>September 2009<meta content="September 2009" itemprop="datePublished"/></span></em>)Wed Dec 16 11:42:31 CET 2009eisterlehner2009ecmlpkddSeptember243--260CEUR-WS.orgContent-based and Graph-based Tag Suggestion4972009dc09 recommender tag Trada: tree based ranking function adaptationhttps://puma.uni-kassel.de/bibtex/20e3e57ca0edda99c53dc3101ffeaef96/hothohotho2009-06-02T11:05:05+02:00ranking toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Keke Chen" itemprop="url" href="/author/Keke%20Chen"><span itemprop="name">K. 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for More Effective Personalizationhttps://puma.uni-kassel.de/bibtex/2a3df32b7e49416d846a7360a861d7cf6/hothohotho2005-12-20T20:21:42+01:00imported <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. Mobasher" itemprop="url" href="/author/B.%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="H. Dai" itemprop="url" href="/author/H.%20Dai"><span itemprop="name">H. Dai</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Luo" itemprop="url" href="/author/T.%20Luo"><span itemprop="name">T. Luo</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Y. Sun" itemprop="url" href="/author/Y.%20Sun"><span itemprop="name">Y. Sun</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Zhu" itemprop="url" href="/author/J.%20Zhu"><span itemprop="name">J. Zhu</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the
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