Result highlight

Hybrid collaboration recommendation from bibliometric data


Medical product development is becoming more and more complex and requires highly-specialized and interdisciplinary collaborations. Their success relies essentially on the selection of suitable partners. However, how to find suitable partners and how to match capabilities of an unknown partner with complex project requirements? Suitability must at least be judged with respect to professional competencies, collaboration capability and project-specific requirements — none of which are easily determined. So, partner selection is mostly dominated by regional proximity or even coincidence. This is a typical scenario for recommender systems. Therefore, we aim at discovering the unexploited potential of collaboration part- ners by proposing a novel recommendation approach that merges trust with health-sensitive semantic information. This hybrid ap- proach should help to identify collaborators matching complex project requirements faster, better and more holistically.

In: Second International Workshop on Health Recommender Systems, pp. 36