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SemRevRec: A Recommender System based on User Reviews and Linked Data

Vagliano, Iacopo; Monti, Diego; Morisio, Maurizio


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  <identifier identifierType="DOI">10.5281/zenodo.1157832</identifier>
  <creators>
    <creator>
      <creatorName>Vagliano, Iacopo</creatorName>
      <givenName>Iacopo</givenName>
      <familyName>Vagliano</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3066-9464</nameIdentifier>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
    <creator>
      <creatorName>Monti, Diego</creatorName>
      <givenName>Diego</givenName>
      <familyName>Monti</familyName>
      <affiliation>Politecnico di Torino</affiliation>
    </creator>
    <creator>
      <creatorName>Morisio, Maurizio</creatorName>
      <givenName>Maurizio</givenName>
      <familyName>Morisio</familyName>
      <affiliation>Politecnico di Torino</affiliation>
    </creator>
  </creators>
  <titles>
    <title>SemRevRec: A Recommender System based on User Reviews and Linked Data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-01-23</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Other</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1157832</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1157831</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://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;Traditionally, recommender systems exploit user ratings to infer preferences. However, the growing popularity of social platforms has encouraged users to write textual reviews about liked items. These reviews represent a valuable source of non-trivial information that could improve users&amp;#39; decision processes. In this paper we propose a novel recommendation approach based on the semantic annotation of entities mentioned in user reviews and on the knowledge available in the Web of Data. We compared our recommender system with two baseline algorithms and a state-of-the-art Linked Data based approach. Our system provided more diverse recommendations with respect to the other techniques considered, while obtaining a better accuracy than the Linked Data based method.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</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>
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