PUMA publications for /author/Nikolas%20Landiahttps://puma.uni-kassel.de/author/Nikolas%20LandiaPUMA RSS feed for /author/Nikolas%20Landia2024-03-29T03:20:14+01:00Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/stummestumme2013-12-16T17:19:49+01:00tagging graph itegpub recommender collaborative social l3s folksonomy bookmarking 2013 iteg folkrank <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 Recommendations1310.14982013tagging graph itegpub recommender collaborative social l3s folksonomy bookmarking 2013 iteg folkrank 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/stephandoerfelstephandoerfel2013-10-13T05:19:47+02:00graph collaborative folksonomy bookmarking 2013 myown folkrank <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 Recommendations1310.14982013graph collaborative folksonomy bookmarking 2013 myown folkrank 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+02:00tagging graph recommender collaborative social folksonomy bookmarking 2013 myown folkrank <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 Recommendations1310.14982013tagging graph recommender collaborative social folksonomy bookmarking 2013 myown folkrank 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.Extending FolkRank with content datahttps://puma.uni-kassel.de/bibtex/2200a05b24a08dd33e377838ae5bdcf71/stummestumme2013-03-18T15:01:54+01:00itegpub extending 2012 folkrank content <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>2012<meta content="2012" itemprop="datePublished"/></span></em>)Mon Mar 18 15:01:54 CET 2013New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social web1--8RSWeb '12Extending FolkRank with content data2012itegpub extending 2012 folkrank content 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 dataExtending FolkRank with content datahttps://puma.uni-kassel.de/bibtex/2200a05b24a08dd33e377838ae5bdcf71/stephandoerfelstephandoerfel2012-09-17T13:23:11+02:00itegpub extending info20pub myown 2012 folkrank content <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>2012<meta content="2012" itemprop="datePublished"/></span></em>)Mon Sep 17 13:23:11 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social web1--8RSWeb '12Extending FolkRank with content data2012itegpub extending info20pub myown 2012 folkrank content 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 dataExtending FolkRank with content datahttps://puma.uni-kassel.de/bibtex/2a97bf903435d6fc4fc61e2bb7e3913b9/hothohotho2012-09-15T12:35:32+02:00tagging social folksonomy bookmarking myown 2012 folkrank <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 data2012tagging social folksonomy bookmarking myown 2012 folkrank 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 dataExtending FolkRank with Content Datahttps://puma.uni-kassel.de/bibtex/2b16dabcd7e17b673c34608ac820ce3c7/jaeschkejaeschke2012-09-06T21:52:34+02:00tagging recommender collaborative social bookmarking myown 2012 folkrank <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 Data2012tagging recommender collaborative social bookmarking myown 2012 folkrank 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.