This paper proposes a novel approach to automated music recommendation systems. Current systems use a number of methods, although these are generally based on similarity of content, contextual information or user ratings. These approaches therefore do not take into account relevant, well-established models from the field of music psychology. Given recent evidence of this field’s excellent capacity to predict music preference, we propose a function based on both the Ebbinghaus forgetting curve of memory retention and Berlyne’s inverted-U model to inform recommendation systems through “collative variables” such as exposure/familiarity. According to the model, an intermediate level of these variables should generate relatively high preference and therefore presents significant untapped data for music recommendation systems.

中文翻译：

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使用记忆存储和偏好的心理学原理来改进音乐推荐系统

本文提出了一种自动音乐推荐系统的新方法。当前的系统使用多种方法，尽管这些方法通常基于内容、上下文信息或用户评级的相似性。因此，这些方法没有考虑来自音乐心理学领域的相关的、完善的模型。鉴于该领域在预测音乐偏好方面具有出色能力的最新证据，我们提出了一个基于记忆保留的艾宾浩斯遗忘曲线和 Berlyne 的倒 U 模型的函数，以通过诸如曝光/熟悉度等“排序变量”通知推荐系统。根据该模型，这些变量的中间级别应该会产生相对较高的偏好，因此为音乐推荐系统提供了大量未开发的数据。