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.

This content is only available as a PDF.
You do not currently have access to this content.