Exploring (Non-) Reproducibility in Recommender-Systems Research
By Joeran Beel
Abstract
Reproducibility is the cornerstone of science. However, in the recommender-system community, reproducibility is widely neglected, and it remains unknown, which factors affect reproducibility.
Consequently, there are many instances of unreproducible research. My research goal is to explore, which factors are responsible for (non-) reproducibility in recommender-systems research. To achieve this goal, I will implement different recommendation approaches, vary the recommendation scenarios, and evaluate the approaches’ effectiveness with different evaluation methods. These variations in recommendation approaches, scenarios, and evaluation methods will allow me to identify which variations affected the recommendation effectiveness, to what extent, and hence affect the reproducibility of recommender-systems research.
