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User-item reciprocity in recommender systems: incentivizing the crowd

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

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.
Published in: UMAP 2014 extended proceedings : posters, demos, late-breaking results and workshop proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization, Aalborg, Denmark, July 7-11, 2014, RWTH