Conference paper Open Access

Profiling vs. Time vs. Content: What Does Matter for Top-k Publication Recommendation Based on Twitter Profiles?

Nishioka, Chifumi; Scherp, Ansgar


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  <identifier identifierType="URL">https://zenodo.org/record/61391</identifier>
  <creators>
    <creator>
      <creatorName>Nishioka, Chifumi</creatorName>
      <givenName>Chifumi</givenName>
      <familyName>Nishioka</familyName>
      <affiliation>Kiel University and Leibniz Information Centre for Economics (ZBW)</affiliation>
    </creator>
    <creator>
      <creatorName>Scherp, Ansgar</creatorName>
      <givenName>Ansgar</givenName>
      <familyName>Scherp</familyName>
      <affiliation>Kiel University and Leibniz Information Centre for Economics (ZBW)</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Profiling vs. Time vs. Content: What Does Matter for Top-k Publication Recommendation Based on Twitter Profiles?</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2016</publicationYear>
  <subjects>
    <subject>recommender system</subject>
    <subject>social media</subject>
    <subject>user profiling</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2016-06-19</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/61391</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/2910896.2910898</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ecfunded</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;So far it is unclear how different factors of a scientific publication recommender system based on users' tweets have an influence on the recommendation performance. We examine three different factors, namely profiling method, temporal decay, and richness of content. Regarding profiling, we compare CF-IDF that replaces terms in TF-IDF by semantic concepts, HCF-IDF as novel hierarchical variant of CF-IDF, and topic modeling. As temporal decay functions, we apply sliding window and exponential decay. In terms of the richness of content, we compare recommendations using both full-texts and titles of publications and using only titles. Overall, the three factors make twelve recommendation strategies. We have conducted an online experiment with 123 participants and compared the strategies in a within-group design. The best recommendations are achieved by the strategy combining CF-IDF, sliding window, and with full-texts. However, the strategies using the novel HCF-IDF profiling method achieve similar results with just using the titles of the publications. Therefore, HCF-IDF can make recommendations when only short and sparse data is available.&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>
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