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  • Autor
    • Ulz, Thomas
    • Schwarz, Michael
    • Felfernig, Alexander
    • Haas, Sarah
    • Shehadeh, Amal
    • Reiterer, Stefan
    • Stettinger, Martin
  • TitelHuman computation for constraint-based recommenders
  • Datei
  • DOI10.1007/s10844-016-0433-4
  • Persistent Identifier
  • Erschienen inJournal of Intelligent Information Systems
  • Band49
  • Erscheinungsjahr2017
  • Heft1
  • Seiten37-57
  • ISSN1573-7675
  • ZugriffsrechteCC-BY
  • Download Statistik511
  • Peer ReviewJa
  • AbstractPeopleViews is a Human Computation based environment for the construction of constraint-based recommenders. Constraint-based recommender systems support the handling of complex items where constraints (e.g., between user requirements and item properties) can be taken into account. When applying such systems, users are articulating their requirements and the recommender identifies solutions on the basis of the constraints in a recommendation knowledge base. In this paper, we provide an overview of the PeopleViews environment and show how recommendation knowledge can be collected from users of the environment on the basis of micro-tasks. We also show how PeopleViews exploits this knowledge for automatically generating recommendation knowledge bases. In this context, we compare the prediction quality of the recommendation approaches integrated in PeopleViews using a DSLR camera dataset.