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.

翻译：在本文中，我们提出了一种受心理学启发的方法，通过利用人类记忆过程对不同用户群体的音乐流派偏好进行建模和预测。这些记忆过程描述了人类如何考虑（i）过去使用频率、（ii）过去使用新近性以及（iii）当前情境等因素，从而访问记忆中的信息单元。利用音乐流媒体平台Last.fm上公开的超过十亿条音乐收听记录数据集，我们发现，对于所有评估的用户群体（即（i）低主流音乐听众、（ii）中主流音乐听众和（iii）高主流音乐听众），我们的方法均显著优于各种基线算法的预测准确率。此外，我们的方法基于一个简单的心理学模型，这有助于提高计算预测的透明性和可解释性。