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Graph Complexity in visual recommender systems for scientific literature

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2016

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Gesellschaft für Informatik e.V.

Zusammenfassung

Digital 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.

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Abels, Stephan; Greven, Christoph; Valdez, André Calero; Schroeder, Ulrik; Ziefle, Martina (2016): Graph Complexity in visual recommender systems for scientific literature. Mensch und Computer 2016 – Workshopband. DOI: 10.18420/muc2016-ws11-0005. Aachen: Gesellschaft für Informatik e.V.. Human Factors in Information Visualization and Decision Support System. Aachen. 4.-7. September 2016

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