PUMA publications for /author/Abdulmotaleb%20El%20Saddikhttps://puma.uni-kassel.de/author/Abdulmotaleb%20El%20SaddikPUMA RSS feed for /author/Abdulmotaleb%20El%20Saddik2024-03-29T03:33:04+01:00Personalized PageRank vectors for tag recommendations: inside FolkRankhttps://puma.uni-kassel.de/bibtex/2f022e60c5928e01c701d7ec539ec221b/stephandoerfelstephandoerfel2012-09-19T21:35:35+02:00inside recommender folkrank recommendation <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Heung-Nam Kim" itemprop="url" href="/author/Heung-Nam%20Kim"><span itemprop="name">H. Kim</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Abdulmotaleb El Saddik" itemprop="url" href="/author/Abdulmotaleb%20El%20Saddik"><span itemprop="name">A. El Saddik</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the fifth ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">45--52</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Wed Sep 19 21:35:35 CEST 2012New York, NY, USAProceedings of the fifth ACM conference on Recommender systems45--52RecSys '11Personalized PageRank vectors for tag recommendations: inside FolkRank2011inside recommender folkrank recommendation This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.Personalized PageRank vectors for tag recommendationsPersonalized PageRank vectors for tag recommendations: inside FolkRankhttps://puma.uni-kassel.de/bibtex/2f022e60c5928e01c701d7ec539ec221b/hothohotho2012-07-14T11:07:20+02:00collaborative bookmarking folkrank toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Heung-Nam Kim" itemprop="url" href="/author/Heung-Nam%20Kim"><span itemprop="name">H. Kim</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Abdulmotaleb El Saddik" itemprop="url" href="/author/Abdulmotaleb%20El%20Saddik"><span itemprop="name">A. El Saddik</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the fifth ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">45--52</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Sat Jul 14 11:07:20 CEST 2012New York, NY, USAProceedings of the fifth ACM conference on Recommender systems45--52Personalized PageRank vectors for tag recommendations: inside FolkRank2011collaborative bookmarking folkrank toread This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.Personalized PageRank vectors for tag recommendations: inside FolkRankhttps://puma.uni-kassel.de/bibtex/2f022e60c5928e01c701d7ec539ec221b/jaeschkejaeschke2011-12-21T22:52:09+01:00tagging pagerank collaborative ranking folksonomy bookmarking search folkrank web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Heung-Nam Kim" itemprop="url" href="/author/Heung-Nam%20Kim"><span itemprop="name">H. Kim</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Abdulmotaleb El Saddik" itemprop="url" href="/author/Abdulmotaleb%20El%20Saddik"><span itemprop="name">A. El Saddik</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the fifth ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">45--52</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Wed Dec 21 22:52:09 CET 2011New York, NY, USAProceedings of the fifth ACM conference on Recommender systems45--52Personalized PageRank vectors for tag recommendations: inside FolkRank2011tagging pagerank collaborative ranking folksonomy bookmarking search folkrank web This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.