Recommender System Augmentation of HR Databases for Team Recommendation

New paradigms for distributed, cross-organizational collaborations are emerging, which e. g. enable open source projects or open innovation. When initiating such projects it can be challenging to choose an appropriate cross-organizational team. For this purpose IT support that uses existing human resource databases can be beneficial for accomplishing this task. We address this by designing a transparent and easy to use recommender that augments skill databases in order to facilitate project managers when composing teams. First we analyze which aspects are relevant for this task by interviewing experts. Then we operationalize these with the help of a meta model developed in our previous work. After that, we propose a concept for team recommendation that considers the above aspects and augments skill databases. Finally, we implement our approach and evaluate it by means of runtime performance.