Using Psychological Principles of Memory Storage and Preference to Improve Music Recommendation Systems

Bibliographic Data

ID20486874
AuthorsAgnieszka Chmiel (0000-0003-3294-0534, Anthony Chmiel (researcher), Empirical Musicology Lab, University of New South Wales, Sydney, Australia, 2052.., corresponding author), Emery Schubert (0000-0002-6541-8083, Emery Schubert (researcher), Empirical Musicology Lab, University of New South Wales, Sydney, Australia, 2052.., corresponding author)
Year2018
Volume28
Pages77-81
Publication date2018-12-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueLeonardo Music Journal (JOURNAL)
Journal identifiersISSN: 0961-1215 • E-ISSN: 1531-4812
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/lmj_a_01045
OpenAlexW2891130554
LanguageEN
Citations received1
References cited3

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

Cognitive psychology · Forgetting · Information retrieval · Preference · Recommender system · Statistics · Computer Science · Mathematics · Music and Audio Processing · Music Technology and Sound Studies · Neuroscience and Music Perception · Psychology · Artificial Intelligence

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