Conference paper Open Access

Reranking-based Recommender System with Deep Learning

Saleh, Ahmed; Mai, Florian; Nishioka, Chifumi; Scherp, Ansgar


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  <dc:creator>Saleh, Ahmed</dc:creator>
  <dc:creator>Mai, Florian</dc:creator>
  <dc:creator>Nishioka, Chifumi</dc:creator>
  <dc:creator>Scherp, Ansgar</dc:creator>
  <dc:date>2018-01-04</dc:date>
  <dc:description>An enormous volume of scientific content is published every year.The amount exceeds by far what a scientist can read in her entire life.In order to address this problem, we have developed and empirically evaluated a recommender system for scientific papers based on Twitter postings. In this paper, we improve on the previous work by a reranking approach using Deep Learning. Thus, after a list of top-k recommendations is computed, we rerank the results by employing a neural network to improve the results of the existing recommender system. We present the design of the deep reranking approach and a preliminary evaluation. Our results show that in most cases, the recommendations can be improved using our Deep Learning reranking approach.</dc:description>
  <dc:identifier>https://zenodo.org/record/1135136</dc:identifier>
  <dc:identifier>10.5281/zenodo.1135136</dc:identifier>
  <dc:identifier>oai:zenodo.org:1135136</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/693092/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.1135135</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/moving-h2020</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>recommender systems</dc:subject>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>semantic profiling</dc:subject>
  <dc:title>Reranking-based Recommender System with Deep Learning</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>
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