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You like Netflix and wonder how they know which movies you like? Learn how develop Recommender Systems

Master Thesis Recommender Systems in Organizations


The collaboration between humans and machines is becoming more and more important. Leveraging the computational power of machines and combine it with the creative mind of humans is seen by many experts as the future. Thus a new field of research emerged which is called human-machine collaboration. In this collaboration, machines play often the role of providing recommendations to humans, on which the latter base their decisions on.

My field of research focuses on how recommender systems can be is used in organizations and in particular in agile teams.

Master Thesis:

Your master thesis aims to conduct qualitative interviews with agile teams to develop design principles for recommender systems.

- Design Science Research and qualitative interviews
- Experimental design

- Develop design principles of recommender systems in the context of organizations
- Design user interfaces and user engagement
- Evaluate the prototype in an experiment

- Close supervision, with regular review meetings, feedback discussions, etc.
- Work can start immediately, but should be completed within the next ±6 months
- If the work is worth publishing, you will be listed as author
- Past successes of students:
- Best master thesis in e-learning (Austria, Germany, Switzerland)
- Papers published at A and B-conferences (e.g. San Francisco and Stockholm)
- Best paper nomination

If you are interested, please contact me: roman.rietsche@unisg.ch

Publiziert von roman.rietsche@unisg.ch

Kontakt: Roman Rietsche