  Digital libraries in the scientific domain provide users access to a wide
range of information to satisfy their diverse information needs. Here, ranking
results play a crucial role in users' satisfaction. Exploiting bibliometric
metadata, e.g., publications' citation counts or bibliometric indicators in
general, for automatically identifying the most relevant results can boost
retrieval performance. This work proposes bibliometric data fusion, which
enriches existing systems' results by incorporating bibliometric metadata such
as citations or altmetrics. Our results on three biomedical retrieval
benchmarks from TREC Precision Medicine (TREC-PM) show that bibliometric data
fusion is a promising approach to improve retrieval performance in terms of
normalized Discounted Cumulated Gain (nDCG) and Average Precision (AP), at the
cost of the Precision at 10 (P@10) rate. Patient users especially profit from
this lightweight, data-sparse technique that applies to any digital library.
