  Data and algorithm sharing is an imperative part of data and AI-driven
economies. The efficient sharing of data and algorithms relies on the active
interplay between users, data providers, and algorithm providers. Although
recommender systems are known to effectively interconnect users and items in
e-commerce settings, there is a lack of research on the applicability of
recommender systems for data and algorithm sharing. To fill this gap, we
identify six recommendation scenarios for supporting data and algorithm
sharing, where four of these scenarios substantially differ from the
traditional recommendation scenarios in e-commerce applications. We evaluate
these recommendation scenarios using a novel dataset based on interaction data
of the OpenML data and algorithm sharing platform, which we also provide for
the scientific community. Specifically, we investigate three types of
recommendation approaches, namely popularity-, collaboration-, and
content-based recommendations. We find that collaboration-based recommendations
provide the most accurate recommendations in all scenarios. Plus, the
recommendation accuracy strongly depends on the specific scenario, e.g.,
algorithm recommendations for users are a more difficult problem than algorithm
recommendations for datasets. Finally, the content-based approach generates the
least popularity-biased recommendations that cover the most datasets and
algorithms.
