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Anthony Chmiel
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Journal Articles
Publisher: Journals Gateway
Leonardo Music Journal (2018) 28: 77–81.
Published: 01 December 2018
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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.