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dc.contributor.authorAbels, Stephan
dc.contributor.authorGreven, Christoph
dc.contributor.authorValdez, André Calero
dc.contributor.authorSchroeder, Ulrik
dc.contributor.authorZiefle, Martina
dc.contributor.editorWeyers, Benjamin
dc.contributor.editorDittmar, Anke
dc.date.accessioned2017-06-17T20:19:19Z
dc.date.available2017-06-17T20:19:19Z
dc.date.issued2016
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/333
dc.description.abstractDigital libraries are becoming larger, while suffering from inefficient interfaces and search patterns. Recommender Systems are a sensible and important service for users of digital libraries. The aim of recommender systems is to reduce cognitive effort, simplify search and to embed results in a larger context. In this article we compare to recommender systems – the Action Science Explorer and Papercube. Both systems are used to recommend scientific literature and use graph-based approaches. From user studies we derive the need for research to understand complexity of graphs.
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2016 – Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.titleGraph Complexity in visual recommender systems for scientific literature
dc.typeworkshop
dc.pubPlaceAachen
mci.document.qualitydigidocde_DE
mci.conference.sessiontitleHuman Factors in Information Visualization and Decision Support System
mci.conference.locationAachen
mci.conference.date4.-7. September 2016
dc.identifier.doi10.18420/muc2016-ws11-0005


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