June 2, 2019 Book section Closed Access

Enrico Palumbo; Alberto Buzio; Andrea Gaiardo; Giuseppe Rizzo; Raphael Troncy; Elena Baralis

DataCite XML Export

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  <identifier identifierType="URL">https://www.openaccessrepository.it/record/31222</identifier>
  <creators>
    <creator>
      <creatorName>Enrico Palumbo</creatorName>
    </creator>
    <creator>
      <creatorName>Alberto Buzio</creatorName>
    </creator>
    <creator>
      <creatorName>Andrea Gaiardo</creatorName>
    </creator>
    <creator>
      <creatorName>Giuseppe Rizzo</creatorName>
    </creator>
    <creator>
      <creatorName>Raphael Troncy</creatorName>
    </creator>
    <creator>
      <creatorName>Elena Baralis</creatorName>
    </creator>
  </creators>
  <titles>
    <title>TinderBook: Fall in love with culture</title>
  </titles>
  <publisher>INFN Open Access Repository</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-06-02</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Book section</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-030-21348-0_38</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0</rights>
    <rights rightsURI="info:eu-repo/semantics/closedAccess">Closed Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">More than 2 millions of new books are published every year and choosing a good book among the huge amount of available options can be a challenging endeavor. Recommender systems help in choosing books by providing personalized suggestions based on the user reading history. However, most book recommender systems are based on collaborative filtering, involving a long onboarding process that requires to rate many books before providing good recommendations. Tinderbook provides book recommendations, given a single book that the user likes, through a card-based playful user interface that does not require an account creation. Tinderbook is strongly rooted in semantic technologies, using the DBpedia knowledge graph to enrich book descriptions and extending a hybrid state-of-the-art knowledge graph embeddings algorithm to derive an item relatedness measure for cold start recommendations. Tinderbook is publicly available (http://www.tinderbook.it) and has already generated interest in the public, involving passionate readers, students, librarians, and researchers. The online evaluation shows that Tinderbook achieves almost 50% of precision of the recommendations.</description>
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