  Financial asset recommendation (FAR) is a sub-domain of recommender systems
which identifies useful financial securities for investors, with the
expectation that they will invest capital on the recommended assets. FAR
solutions analyse and learn from multiple data sources, including time series
pricing data, customer profile information and expectations, as well as past
investments. However, most models have been developed over proprietary
datasets, making a comparison over a common benchmark impossible. In this
paper, we aim to solve this problem by introducing FAR-Trans, the first public
dataset for FAR, containing pricing information and retail investor
transactions acquired from a large European financial institution. We also
provide a bench-marking comparison between eleven FAR algorithms over the data
for use as future baselines. The dataset can be downloaded from
https://doi.org/10.5525/gla.researchdata.1658 .
