User-item reciprocity in recommender systems: Incentivizing the crowd

Alan Said, Martha Larson, Domonkos Tikk, Paolo Cremonesi, Alexandros Karatzoglou, Frank Hopfgartner, Roberto Turrin, Joost Geurts

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

Data consumption has changed significantly in the last 10 years. The digital revolution and the Internet has brought an abundance of information to users. Recommender systems are a popular means of finding content that is both relevant and personalized. However, today's users require better recommender systems, able of producing continuous data feeds keeping up with their instantaneous and mobile needs. The CrowdRec project addresses this demand by providing context-aware, resource-combining, socially-informed, interactive and scalable recommendations. The key insight of CrowdRec is that, in order to achieve the dense, high-quality, timely information required for such systems, it is necessary to move from passive user data collection, to more active techniques fostering user engagement. For this purpose, CrowdRec activates the crowd, soliciting input and feedback from the wider community.

Original languageEnglish
Pages (from-to)23-26
Number of pages4
JournalCEUR Workshop Proceedings
Volume1181
Publication statusPublished - Jan 1 2014
EventWorkshop of the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Aalborg, Denmark
Duration: Jul 7 2014Jul 11 2014

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Said, A., Larson, M., Tikk, D., Cremonesi, P., Karatzoglou, A., Hopfgartner, F., Turrin, R., & Geurts, J. (2014). User-item reciprocity in recommender systems: Incentivizing the crowd. CEUR Workshop Proceedings, 1181, 23-26.