Skip Nav Destination
1-1 of 1
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Using Psychological Principles of Memory Storage and Preference to Improve Music Recommendation Systems
Publisher: Journals Gateway
Leonardo Music Journal (2018) 28: 77–81.
Published: 01 December 2018
AbstractView article PDF
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.