Conference paper Open Access

Reranking-based Recommender System with Deep Learning

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


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.1135136</identifier>
  <creators>
    <creator>
      <creatorName>Saleh, Ahmed</creatorName>
      <givenName>Ahmed</givenName>
      <familyName>Saleh</familyName>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
    <creator>
      <creatorName>Mai, Florian</creatorName>
      <givenName>Florian</givenName>
      <familyName>Mai</familyName>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
    <creator>
      <creatorName>Nishioka, Chifumi</creatorName>
      <givenName>Chifumi</givenName>
      <familyName>Nishioka</familyName>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
    <creator>
      <creatorName>Scherp, Ansgar</creatorName>
      <givenName>Ansgar</givenName>
      <familyName>Scherp</familyName>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Reranking-based Recommender System with Deep Learning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>recommender systems</subject>
    <subject>deep learning</subject>
    <subject>semantic profiling</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-01-04</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1135136</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1135135</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/693092/">693092</awardNumber>
      <awardTitle>Training towards a society of data-savvy information professionals to enable open leadership innovation</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
30
17
views
downloads
All versions This version
Views 3030
Downloads 1717
Data volume 2.8 MB2.8 MB
Unique views 3030
Unique downloads 1616

Share

Cite as