  In this paper, we introduce a psychology-inspired approach to model and
predict the music genre preferences of different groups of users by utilizing
human memory processes. These processes describe how humans access information
units in their memory by considering the factors of (i) past usage frequency,
(ii) past usage recency, and (iii) the current context. Using a publicly
available dataset of more than a billion music listening records shared on the
music streaming platform Last.fm, we find that our approach provides
significantly better prediction accuracy results than various baseline
algorithms for all evaluated user groups, i.e., (i) low-mainstream music
listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream
music listeners. Furthermore, our approach is based on a simple psychological
model, which contributes to the transparency and explainability of the
calculated predictions.
