PUMA publications for /tag/bookmarkinghttps://puma.uni-kassel.de/tag/bookmarkingPUMA RSS feed for /tag/bookmarking2024-03-30T05:34:41+01:00On Publication Usage in a Social Bookmarking Systemhttps://puma.uni-kassel.de/bibtex/2548a7010ee2726f28e04e5c6e5fd6e2d/hothohotho2015-07-24T18:10:28+02:002015 altmetrics bookmarking impact myown publication social usage <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Zoller" itemprop="url" href="/author/Daniel%20Zoller"><span itemprop="name">D. Zoller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2015 ACM Conference on Web Science</span>, </em></span>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)Fri Jul 24 18:10:28 CEST 2015Proceedings of the 2015 ACM Conference on Web ScienceOn Publication Usage in a Social Bookmarking System20152015 altmetrics bookmarking impact myown publication social usage Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.Altmetrics in the wild: Using social media to explore scholarly impacthttps://puma.uni-kassel.de/bibtex/2e22613ac29fd25f21430739a4c3e001c/stephandoerfelstephandoerfel2015-03-16T13:30:53+01:00altmetrics bookmarking citations correlation coverage impact wild <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jason Priem" itemprop="url" href="/author/Jason%20Priem"><span itemprop="name">J. Priem</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Heather A. Piwowar" itemprop="url" href="/author/Heather%20A.%20Piwowar"><span itemprop="name">H. Piwowar</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bradley M. Hemminger" itemprop="url" href="/author/Bradley%20M.%20Hemminger"><span itemprop="name">B. Hemminger</span></a></span>. </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)<em>cite arxiv:1203.4745v1Comment: 5 tables, 13 figures.</em>Mon Mar 16 13:30:53 CET 2015cite arxiv:1203.4745v1Comment: 5 tables, 13 figuresAltmetrics in the wild: Using social media to explore scholarly impact2012altmetrics bookmarking citations correlation coverage impact wild In growing numbers, scholars are integrating social media tools like blogs,
Twitter, and Mendeley into their professional communications. The online,
public nature of these tools exposes and reifies scholarly processes once
hidden and ephemeral. Metrics based on this activities could inform broader,
faster measures of impact, complementing traditional citation metrics. This
study explores the properties of these social media-based metrics or
"altmetrics", sampling 24,331 articles published by the Public Library of
Science.
We find that that different indicators vary greatly in activity. Around 5% of
sampled articles are cited in Wikipedia, while close to 80% have been included
in at least one Mendeley library. There is, however, an encouraging diversity;
a quarter of articles have nonzero data from five or more different sources.
Correlation and factor analysis suggest citation and altmetrics indicators
track related but distinct impacts, with neither able to describe the complete
picture of scholarly use alone. There are moderate correlations between
Mendeley and Web of Science citation, but many altmetric indicators seem to
measure impact mostly orthogonal to citation. Articles cluster in ways that
suggest five different impact "flavors", capturing impacts of different types
on different audiences; for instance, some articles may be heavily read and
saved by scholars but seldom cited. Together, these findings encourage more
research into altmetrics as complements to traditional citation measures.[1203.4745v1] Altmetrics in the wild: Using social media to explore scholarly impactThe Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metricshttps://puma.uni-kassel.de/bibtex/2221dd554089fd1b1918b345fffbd74ce/stephandoerfelstephandoerfel2015-03-16T12:10:45+01:00bookmarking citation correlation plosOne webometrics <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Koon-Kiu Yan" itemprop="url" href="/author/Koon-Kiu%20Yan"><span itemprop="name">K. Yan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mark Gerstein" itemprop="url" href="/author/Mark%20Gerstein"><span itemprop="name">M. Gerstein</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>PLoS ONE</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">6 </span></span>(<span itemprop="issueNumber">5</span>):
<span itemprop="pagination">e19917</span></em> </span>(<em><span>Mai 2011<meta content="Mai 2011" itemprop="datePublished"/></span></em>)Mon Mar 16 12:10:45 CET 2015PLoS ONE055e19917The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics62011bookmarking citation correlation plosOne webometrics <p>The presence of web-based communities is a distinctive signature of Web 2.0. The web-based feature means that information propagation within each community is highly facilitated, promoting complex collective dynamics in view of information exchange. In this work, we focus on a community of scientists and study, in particular, how the awareness of a scientific paper is spread. Our work is based on the web usage statistics obtained from the PLoS Article Level Metrics dataset compiled by PLoS. The cumulative number of HTML views was found to follow a long tail distribution which is reasonably well-fitted by a lognormal one. We modeled the diffusion of information by a random multiplicative process, and thus extracted the rates of information spread at different stages after the publication of a paper. We found that the spread of information displays two distinct decay regimes: a rapid downfall in the first month after publication, and a gradual power law decay afterwards. We identified these two regimes with two distinct driving processes: a short-term behavior driven by the fame of a paper, and a long-term behavior consistent with citation statistics. The patterns of information spread were found to be remarkably similar in data from different journals, but there are intrinsic differences for different types of web usage (HTML views and PDF downloads versus XML). These similarities and differences shed light on the theoretical understanding of different complex systems, as well as a better design of the corresponding web applications that is of high potential marketing impact.</p>
Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papershttps://puma.uni-kassel.de/bibtex/2677fc89fef6c79a6a4f25cb25246e38a/stephandoerfelstephandoerfel2015-03-09T17:58:58+01:00bookmarking citation prediction scientometrics social tagging www <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A.U. Saeed" itemprop="url" href="/author/A.U.%20Saeed"><span itemprop="name">A. Saeed</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M.T. Afzal" itemprop="url" href="/author/M.T.%20Afzal"><span itemprop="name">M. Afzal</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Latif" itemprop="url" href="/author/A.%20Latif"><span itemprop="name">A. Latif</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="K. Tochtermann" itemprop="url" href="/author/K.%20Tochtermann"><span itemprop="name">K. Tochtermann</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Multitopic Conference, 2008. INMIC 2008. IEEE International</span>, </em></span><em>Seite <span itemprop="pagination">392-397</span>. </em>(<em><span>Dezember 2008<meta content="Dezember 2008" itemprop="datePublished"/></span></em>)Mon Mar 09 17:58:58 CET 2015Multitopic Conference, 2008. INMIC 2008. IEEE Internationaldec392-397Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papers2008bookmarking citation prediction scientometrics social tagging www New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.IEEE Xplore Abstract - Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papersApplying social bookmarking data to evaluate journal usagehttps://puma.uni-kassel.de/bibtex/2c3e49ee7b0ed81ecd126d3ef76d5f407/stephandoerfelstephandoerfel2015-03-09T14:15:09+01:00bibsonomy bookmarking citations scientometrics social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefanie Haustein" itemprop="url" href="/author/Stefanie%20Haustein"><span itemprop="name">S. Haustein</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tobias Siebenlist" itemprop="url" href="/author/Tobias%20Siebenlist"><span itemprop="name">T. Siebenlist</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>Journal of Informetrics</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">5 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">446 - 457</span></em> </span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Mon Mar 09 14:15:09 CET 2015Journal of Informetrics 3446 - 457Applying social bookmarking data to evaluate journal usage 52011bibsonomy bookmarking citations scientometrics social tagging Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in \{STM\} and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content. Applying social bookmarking data to evaluate journal usageDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/stummestumme2013-12-16T17:19:49+01:002013 bookmarking collaborative folkrank folksonomy graph iteg itegpub l3s recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</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>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Dec 16 17:19:49 CET 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph iteg itegpub l3s recommender social tagging 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.An analysis of tag-recommender evaluation procedureshttps://puma.uni-kassel.de/bibtex/2aa4b3d79a362d7415aaa77625b590dfa/stummestumme2013-12-16T17:19:49+01:002013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 7th ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">343--346</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Dec 16 17:19:49 CET 2013New York, NY, USAProceedings of the 7th ACM conference on Recommender systems343--346RecSys '13An analysis of tag-recommender evaluation procedures20132013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/stephandoerfelstephandoerfel2013-10-13T05:19:47+02:002013 bookmarking collaborative folkrank folksonomy graph myown <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</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>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Sun Oct 13 05:19:47 CEST 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph myown 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.Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/hothohotho2013-10-10T12:27:01+02:002013 bookmarking collaborative folkrank folksonomy graph myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</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>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Thu Oct 10 12:27:01 CEST 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph myown recommender social tagging 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.Information archiving with bookmarks: personal Web space construction and organizationhttps://puma.uni-kassel.de/bibtex/2a9a25a144cec844bcd7daeace4a548aa/stephandoerfelstephandoerfel2013-09-03T21:31:29+02:00analysis bookmarking folksonomy log social usage <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David Abrams" itemprop="url" href="/author/David%20Abrams"><span itemprop="name">D. Abrams</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ron Baecker" itemprop="url" href="/author/Ron%20Baecker"><span itemprop="name">R. Baecker</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mark Chignell" itemprop="url" href="/author/Mark%20Chignell"><span itemprop="name">M. Chignell</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the SIGCHI Conference on Human Factors in Computing Systems</span>, </em></span><em>Seite <span itemprop="pagination">41--48</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM Press/Addison-Wesley Publishing Co.</span>, </em>(<em><span>1998<meta content="1998" itemprop="datePublished"/></span></em>)Tue Sep 03 21:31:29 CEST 2013New York, NY, USAProceedings of the SIGCHI Conference on Human Factors in Computing Systems41--48CHI '98Information archiving with bookmarks: personal Web space construction and organization1998analysis bookmarking folksonomy log social usage Information archiving with bookmarksCharacterizing a social bookmarking and tagging networkhttps://puma.uni-kassel.de/bibtex/202d6739886a13180dd92fbb7243ab58b/jaeschkejaeschke2013-05-09T10:47:35+02:00analysis bookmarking collaborative folksonomy network tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ralitsa Angelova" itemprop="url" href="/author/Ralitsa%20Angelova"><span itemprop="name">R. Angelova</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marek Lipczak" itemprop="url" href="/author/Marek%20Lipczak"><span itemprop="name">M. Lipczak</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Evangelos Milios" itemprop="url" href="/author/Evangelos%20Milios"><span itemprop="name">E. Milios</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paweł Prałat" itemprop="url" href="/author/Pawe%c5%82%20Pra%c5%82at"><span itemprop="name">P. Prałat</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Mining Social Data Workshop (MSoDa)</span>, </em></span><em>Seite <span itemprop="pagination">21--25</span>. </em><em>ECAI 2008, </em>(<em><span>Juli 2008<meta content="Juli 2008" itemprop="datePublished"/></span></em>)Thu May 09 10:47:35 CEST 2013Proceedings of the Mining Social Data Workshop (MSoDa)jul21--25Characterizing a social bookmarking and tagging network2008analysis bookmarking collaborative folksonomy network tagging Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution. Recommender Systems for Social Tagging Systemshttps://puma.uni-kassel.de/bibtex/287d6883ebd98e8810be45d7e7e4ade96/stummestumme2013-03-18T14:06:44+01:002012 bookmarking collaborative folksonomy info20 itegpub l3s myown recommender social tagging tagging,2012 <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Balby Marinho" itemprop="url" href="/author/L.%20Balby%20Marinho"><span itemprop="name">L. Balby Marinho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Jäschke" itemprop="url" href="/author/R.%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Nanopoulos" itemprop="url" href="/author/A.%20Nanopoulos"><span itemprop="name">A. Nanopoulos</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Rendle" itemprop="url" href="/author/S.%20Rendle"><span itemprop="name">S. Rendle</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Schmidt-Thieme" itemprop="url" href="/author/L.%20Schmidt-Thieme"><span itemprop="name">L. Schmidt-Thieme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Symeonidis" itemprop="url" href="/author/P.%20Symeonidis"><span itemprop="name">P. Symeonidis</span></a></span>. </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>Februar 2012<meta content="Februar 2012" itemprop="datePublished"/></span></em>)Mon Mar 18 14:06:44 CET 2013febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy info20 itegpub l3s myown recommender social tagging tagging,2012 Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systemshttps://puma.uni-kassel.de/bibtex/25b6b648fd25c15d594404ae26fcda6b4/stummestumme2013-03-18T14:06:44+01:002008 bookmarking detection itegpub l3s myown seminar spam summer <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Beate Krause" itemprop="url" href="/author/Beate%20Krause"><span itemprop="name">B. Krause</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web</span>, </em></span><em>Seite <span itemprop="pagination">61--68</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>April 2008<meta content="April 2008" itemprop="datePublished"/></span></em>)Mon Mar 18 14:06:44 CET 2013New York, NY, USAAIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Webapr61--68The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems20082008 bookmarking detection itegpub l3s myown seminar spam summer The annotation of web sites in social bookmarking systemshas become a popular way to manage and find informationon the web. The community structure of such systems attractsspammers: recent post pages, popular pages or specifictag pages can be manipulated easily. As a result, searchingor tracking recent posts does not deliver quality resultsannotated in the community, but rather unsolicited, oftencommercial, web sites. To retain the benefits of sharingone’s web content, spam-fighting mechanisms that can facethe flexible strategies of spammers need to be developed.Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendationhttps://puma.uni-kassel.de/bibtex/264bf590675a833770b7d284871435a8d/stummestumme2013-03-18T14:06:44+01:002012 bookmarking collaborative folkrank itegpub l3s myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web</span>, </em></span><em>Seite <span itemprop="pagination">9--16</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>September 2012<meta content="September 2012" itemprop="datePublished"/></span></em>)Mon Mar 18 14:06:44 CET 2013New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep9--16Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation 20122012 bookmarking collaborative folkrank itegpub l3s myown recommender social tagging The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reasonhttps://puma.uni-kassel.de/bibtex/2d09b484b1036ca8273743cac1992dd7f/jaeschkejaeschke2012-10-12T09:08:53+02:00bookmarking cognition collaborative collective intelligence search social tagging web wiki <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ed H. Chi" itemprop="url" href="/author/Ed%20H.%20Chi"><span itemprop="name">E. Chi</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2009 ACM SIGMOD International Conference on Management of data</span>, </em></span><em>Seite <span itemprop="pagination">973--984</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 Oct 12 09:08:53 CEST 2012New York, NY, USAProceedings of the 2009 ACM SIGMOD International Conference on Management of data973--984Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reason2009bookmarking cognition collaborative collective intelligence search social tagging web wiki We are experiencing a new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by systems like Wikipedia, twitter, and delicious.com, these environments are turning people into social information foragers and sharers. Groups interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam."</p> <p>PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, CSCW, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Social Web systems like social bookmarking sites, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.</p> <p>Here we summarize recent work and early findings such as: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.Leveraging publication metadata and social data into FolkRank for scientific publication recommendationhttps://puma.uni-kassel.de/bibtex/2e5c2266da34a9167352615827cc4670d/hothohotho2012-09-15T12:37:17+02:002012 bookmarking folkrank myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web</span>, </em></span><em>Seite <span itemprop="pagination">9--16</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Sat Sep 15 12:37:17 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social web9--16RSWeb '12Leveraging publication metadata and social data into FolkRank for scientific publication recommendation20122012 bookmarking folkrank myown recommender social tagging The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.Leveraging publication metadata and social data into FolkRank for scientific publication recommendationExtending FolkRank with content datahttps://puma.uni-kassel.de/bibtex/2a97bf903435d6fc4fc61e2bb7e3913b9/hothohotho2012-09-15T12:35:32+02:002012 bookmarking folkrank folksonomy myown social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%5c%22%7ba%7dschke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Folke Mitzlaff" itemprop="url" href="/author/Folke%20Mitzlaff"><span itemprop="name">F. Mitzlaff</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web</span>, </em></span><em>Seite <span itemprop="pagination">1--8</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Sat Sep 15 12:35:32 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social web1--8RSWeb '12Extending FolkRank with content data20122012 bookmarking folkrank folksonomy myown social tagging Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.Extending FolkRank with content dataLeveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendationhttps://puma.uni-kassel.de/bibtex/264bf590675a833770b7d284871435a8d/jaeschkejaeschke2012-09-06T21:54:12+02:002012 bookmarking collaborative folkrank myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web</span>, </em></span><em>Seite <span itemprop="pagination">9--16</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>September 2012<meta content="September 2012" itemprop="datePublished"/></span></em>)Thu Sep 06 21:54:12 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep9--16Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation 20122012 bookmarking collaborative folkrank myown recommender social tagging The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.Extending FolkRank with Content Datahttps://puma.uni-kassel.de/bibtex/2b16dabcd7e17b673c34608ac820ce3c7/jaeschkejaeschke2012-09-06T21:52:34+02:002012 bookmarking collaborative folkrank myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Folke Mitzlaff" itemprop="url" href="/author/Folke%20Mitzlaff"><span itemprop="name">F. Mitzlaff</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web</span>, </em></span><em>Seite <span itemprop="pagination">1--8</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>September 2012<meta content="September 2012" itemprop="datePublished"/></span></em>)Thu Sep 06 21:52:34 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep1--8Extending FolkRank with Content Data20122012 bookmarking collaborative folkrank myown recommender social tagging Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags. Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.Extraktion und Visualisierung ortsbezogener Informationen mit Tag-Cloudshttps://puma.uni-kassel.de/bibtex/2a28959724af1907e7fc67a68e648c14c/jaeschkejaeschke2012-09-06T12:34:34+02:00bookmarking cloud collaborative everyaware geo location social tagging visualization <meta content="thesis" itemprop="educationalUse"/><span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Oliver Flohr" itemprop="url" href="/author/Oliver%20Flohr"><span itemprop="name">O. Flohr</span></a></span>. </span><em>Gottfried Wilhelm Leibniz Universität Hannover, </em><em><span itemprop="educationalUse">bachelor thesis</span>, </em>(<em><span>August 2011<meta content="August 2011" itemprop="datePublished"/></span></em>)Thu Sep 06 12:34:34 CEST 2012augExtraktion und Visualisierung ortsbezogener Informationen mit Tag-Cloudsbachelor thesis2011bookmarking cloud collaborative everyaware geo location social tagging visualization Informationen so aufzubereiten, dass sie für eine bestimmte Situation nützlich sind, ist eine große Herausforderung. In solchen Situationen soll ein Benutzer, wenn er sich an einem fremden Ort befindet, mit Hilfe des Android Smartphone interessante und wis- senswerte Informationen anzeigen lassen. Um dies bewerkstelligen zu können, muss es eine georeferenzierte Informationsquelle geben. Außerdem muss ein Konzept vor- handen sein, um diese Daten zu sammeln und so aufzubereiten, dass der Benutzer diese auch nützlich findet. Es muss eine Visualisierung dieser Daten geben, da der Platz zur Anzeige auf Smartphones sehr begrenzt ist. Als georeferenzierte Informationsquelle wird die Online-Enzyklopädie Wikipedia ge- nutzt, diese ist frei zugänglich und auch sehr umfassend. In dieser Arbeit wird das Konzept zur Sammlung und Aufbereitung von relevanten Daten behandelt. Zur In- formationsvisualisierung wird die Methode der Schlagwortwolke (engl. Tag-Cloud) verwendet. It is a major challenge to prepare useful information for a particular situation. In this situation an Android smartphone user wants to display interesting and important facts about an unknown place. To manage this task existence of a geo-referenced source of information has to be ensured. In order to collect and prepare this data a creation of concept is needed. Due to limited display space, it is necessary to construct a suitable visualization of this data. Wikipedia is used as a geo-referenced information resource, because it has open-access and it offers global geo-referenced information. This thesis covers the concept of col- lecting and preparing relevant data. To visualize information a tag cloud is used.