At the Data Science Chair we're researching various topics within the field of recommender system. From classic applications like presenting relevant products to users in an e-commerce setting or recommending tags in our own social bookmarking system BibSonomy, to systems predicting medical examination or decisions in games. We utilize different machine learning algorithms, including deep learning in our work.
The following staff member have open topics for practica, bachelor and master theses:
|HypTrails, Recommendation, Graph Networks for publication data, Regio||Tobias Koopmann|
|recommendation in ecommerce, adidas||Elisabeth Fischer|
|Mathematical Pattern Mining, Representation Learning, Recommender Systems||Sebastian Wankerl|
|Medical and Healthcare Recommendation||Daniel Schlör|
In the case of excellent performance there is also the chance to submit the thesis as an article to a computer science conference and to be co-author on a scientific publication early in your studies!