1 Ocak 2014 Konferans bildirisi Açık Erişim

Orellana-Rodriguez, Claudia; Nejdl, Wolfgang; Diaz-Aviles, Ernesto; Altingovde, Ismail Sengor

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/63085</identifier>
  <creators>
    <creator>
      <creatorName>Orellana-Rodriguez, Claudia</creatorName>
      <givenName>Claudia</givenName>
      <familyName>Orellana-Rodriguez</familyName>
      <affiliation>L3S Res Ctr, Hannover, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Nejdl, Wolfgang</creatorName>
      <givenName>Wolfgang</givenName>
      <familyName>Nejdl</familyName>
      <affiliation>L3S Res Ctr, Hannover, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Diaz-Aviles, Ernesto</creatorName>
      <givenName>Ernesto</givenName>
      <familyName>Diaz-Aviles</familyName>
    </creator>
    <creator>
      <creatorName>Altingovde, Ismail Sengor</creatorName>
      <givenName>Ismail Sengor</givenName>
      <familyName>Altingovde</familyName>
      <affiliation>Middle East Tech Univ, Ankara, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Learning To Rank For Joy</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2014</publicationYear>
  <dates>
    <date dateType="Issued">2014-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/63085</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/2567948.2576961</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">User-generated content is a growing source of valuable information and its analysis can lead to a better understanding of the users needs and trends. In this paper, we leverage user feedback about YouTube videos for the task of affective video ranking. To this end, we follow a learning to rank approach, which allows us to compare the performance of different sets of features when the ranking task goes beyond mere relevance and requires an affective understanding of the videos. Our results show that, while basic video features, such as title and tags, lead to effective rankings in an affective-less setup, they do not perform as good when dealing with an affective ranking task.</description>
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