PUMA publications for /author/Lyle%20H.%20Ungarhttps://puma.uni-kassel.de/author/Lyle%20H.%20UngarPUMA RSS feed for /author/Lyle%20H.%20Ungar2024-03-29T13:53:14+01:00Methods and Metrics for Cold-start Recommendationshttps://puma.uni-kassel.de/bibtex/2eab2ae9f99bd5aed7ee66cd57b1cbc47/stephandoerfelstephandoerfel2014-06-06T18:24:36+02:00recommender metrics start groc cold croc <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andrew I. Schein" itemprop="url" href="/author/Andrew%20I.%20Schein"><span itemprop="name">A. Schein</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexandrin Popescul" itemprop="url" href="/author/Alexandrin%20Popescul"><span itemprop="name">A. Popescul</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lyle H. Ungar" itemprop="url" href="/author/Lyle%20H.%20Ungar"><span itemprop="name">L. Ungar</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David M. Pennock" itemprop="url" href="/author/David%20M.%20Pennock"><span itemprop="name">D. Pennock</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval</span>, </em></span><em>Seite <span itemprop="pagination">253--260</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Fri Jun 06 18:24:36 CEST 2014New York, NY, USAProceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval253--260SIGIR '02Methods and Metrics for Cold-start Recommendations2002recommender metrics start groc cold croc We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.Methods and metrics for cold-start recommendationsStatistical Relational Learning for Document Mining.https://puma.uni-kassel.de/bibtex/27cdd6b0791fcdf17ec6d404b55f12c5c/hothohotho2007-08-31T14:53:26+02:00document 2003 classification text srl tm mining <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexandrin Popescul" itemprop="url" href="/author/Alexandrin%20Popescul"><span itemprop="name">A. Popescul</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lyle H. Ungar" itemprop="url" href="/author/Lyle%20H.%20Ungar"><span itemprop="name">L. Ungar</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steve Lawrence" itemprop="url" href="/author/Steve%20Lawrence"><span itemprop="name">S. Lawrence</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David M. Pennock" itemprop="url" href="/author/David%20M.%20Pennock"><span itemprop="name">D. Pennock</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">ICDM</span>, </em></span><em>Seite <span itemprop="pagination">275-282</span>. </em><em><span itemprop="publisher">IEEE Computer Society</span>, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Fri Aug 31 14:53:26 CEST 2007ICDMconf/icdm/2003275-282Statistical Relational Learning for Document Mining.2003document 2003 classification text srl tm mining dblp