Name
Abstract | ||
|---|---|---|
Discovering relevant publications for researchers is a non-trivial task. Recommender systems can reduce the effort required to find relevant publications. We suggest using a visualization- and user-centered interaction model to achieve both a more trusted recommender system and a system to understand a whole research field. In a graph-based visualization papers are aligned with their keywords according to the relevance of the keywords. Relevance is determined using text-mining approaches. By letting the user control relevance thresholds for individual keywords we have designed a recommender system that scores high in accuracy ((bar{x}=5.03/6)), trust ((bar{x}=4.31/6)) and usability (SUS (bar{x}=4.89/6)) in a user study, while at the same time providing additional information about the field as a whole. As a result, the inherent trust issues conventional recommendation systems have seem to be less significant when using our solution. |
Year | Venue | Field |
|---|---|---|
2015 | HCI | Recommender system,Graph,Public records,User control,Information retrieval,Visualization,Computer science,Usability,Human–computer interaction |
DocType | Citations | PageRank |
Conference | 3 | 0.42 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Bruns | 1 | 3 | 0.42 |
| André Calero Valdez | 2 | 134 | 25.44 |
| Christoph Greven | 3 | 11 | 5.89 |
| Martina Ziefle | 4 | 1176 | 135.05 |
| Ulrik Schroeder | 5 | 291 | 78.85 |