论文标题
利用人类记忆过程为个性化音乐建议建模流派偏好
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
论文作者
论文摘要
在本文中，我们引入了一种受心理学启发的方法，通过利用人类记忆过程来建模和预测不同用户的音乐流派偏好。这些过程描述了人类如何通过考虑（i）过去的使用频率，（ii）过去的用法和（iii）当前上下文的因素来访问记忆中的信息单位。使用公开可用的数据集，该数据集在Last.fm上共享了音乐流媒体平台上共享的10亿多音乐聆听记录，我们发现我们的方法比各种评估的用户组（即（i）低主流音乐听众，（ii）中型中学音乐听众（II）中型音乐听众，（III）高主流音乐听众的各种基线算法提供了明显更好的预测准确性结果。此外，我们的方法基于一个简单的心理模型，这有助于计算预测的透明度和解释性。
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.
