Conference paper Open Access

Reranking-based Recommender System with Deep Learning

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

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <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: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: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>
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