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Project

Overcoming cognitive and motivational barriers for networking: contact recommendation systems in professional settings

Junior Research groupSocial Media
Duration08/2017–06/2020
FundingLeibniz-WissenschaftsCampus "Cognitive Interfaces"
Project description

The project investigates which factors have a positive or negative impact on networking behavior in professional settings, and how these factors can be promoted or attenuated respectively. In addition to studying the influencing factors, an algorithm for recommender systems in professional social media will be developed. The project goal is to make networking on professional social media platforms easier, thereby helping knowledge workers with their work.


A diverse network can provide large career benefits by giving access to non-redundant information, especially for knowledge workers. Networks that are comprised of a number of diverse people provide access to non-redundant information and thereby foster creativity and ideas. Creativity and new ideas are the basis for successful problem solving in the workplace. Active networking and a diverse network can thus influence the worker’s professional competences and his/her professional success. Professionally used social media such as Xing or LinkedIn can help to build and maintain diverse networks. However, many people only make little use of these possibilities. In addition, existing recommender systems, which propose new contacts, usually operate on the basis of similarities such as common contacts and thus recommend people from the direct environment.


The project therefore covers two subareas: In the first step, survey and experimental studies are used to determine influence factors which support or prevent network behavior in professional online networks in order to investigate how these can be promoted or avoided. In the second step, in cooperation with the Department of Computer Science at the University of Tübingen, an algorithm will be developed specifically for professional social media in order to improve recommender systems and incorporate the insights gained in the earlier project step.

Cooperations

Jun.-Prof. Dr. Enkelejda Kasneci (Department of Computer Science, University of Tübingen)

contact

Lea Baumann, M.A. Lea Baumann, M.A.
Tel.: +49 7071 979-320

Project team

Prof. Dr. Sonja Utz