  The search for research datasets is as important as laborious. Due to the
importance of the choice of research data in further research, this decision
must be made carefully. Additionally, because of the growing amounts of data in
almost all areas, research data is already a central artifact in empirical
sciences. Consequentially, research dataset recommendations can beneficially
supplement scientific publication searches. We formulated the recommendation
task as a retrieval problem by focussing on broad similarities between research
datasets and scientific publications. In a multistage approach, initial
recommendations were retrieved by the BM25 ranking function and dynamic
queries. Subsequently, the initial ranking was re-ranked utilizing click
feedback and document embeddings. The proposed system was evaluated live on
real user interaction data using the STELLA infrastructure in the LiLAS Lab at
CLEF 2021. Our experimental system could efficiently be fine-tuned before the
live evaluation by pre-testing the system with a pseudo test collection based
on prior user interaction data from the live system. The results indicate that
the experimental system outperforms the other participating systems.
