Graph Complexity in visual recommender systems for scientific literature
| dc.contributor.author | Abels, Stephan | |
| dc.contributor.author | Greven, Christoph | |
| dc.contributor.author | Valdez, André Calero | |
| dc.contributor.author | Schroeder, Ulrik | |
| dc.contributor.author | Ziefle, Martina | |
| dc.contributor.editor | Weyers, Benjamin | |
| dc.contributor.editor | Dittmar, Anke | |
| dc.date.accessioned | 2017-06-17T20:19:19Z | |
| dc.date.available | 2017-06-17T20:19:19Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | 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. | |
| dc.identifier.doi | 10.18420/muc2016-ws11-0005 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/333 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Mensch und Computer 2016 – Workshopband | |
| dc.relation.ispartofseries | Mensch und Computer | |
| dc.title | Graph Complexity in visual recommender systems for scientific literature | |
| dc.type | Text/Conference Paper | |
| gi.citation.publisherPlace | Aachen | |
| gi.conference.date | 4.-7. September 2016 | |
| gi.conference.location | Aachen | |
| gi.conference.sessiontitle | Human Factors in Information Visualization and Decision Support System | |
| gi.document.quality | digidoc | de_DE |
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