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You are interested in building systems and evaluate them in real worlds settings? Learn how to design Recommender systems

Implement Recommender Systems

Why:

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 develop a recommender system prototype and evaluate it in an experimental setting.

How:
- Design Science Research
- Natural Language Processing
- Machine Learning

Key-Facts:
- 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