Graph Complexity in visual recommender systems for scientific literature
ISSN der Zeitschrift
Mensch und Computer 2016 – Workshopband
Human Factors in Information Visualization and Decision Support System
Gesellschaft für Informatik e.V.
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.