D. Kowald, Elisabeth Lex, Markus Schedl,
"Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations"
: Proceedings of the 25th Conference on Intelligent User Interfaces (IUI 2020): Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE 2020), 3-2020
Original Titel:
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 25th Conference on Intelligent User Interfaces (IUI 2020): Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE 2020)
Original Kurzfassung:
In this paper, we introduce a psychology-inspired approach
to model and predict the music genre preferences of different groups of users by utilizing human memory processes.
These processes describe how humans access information
units in their memory by considering the factors of (i) past
usage frequency, (ii) past usage recency, and (iii) the current
context. Using a publicly available dataset of more than a
billion music listening records shared on the music streaming platform Last.fm, we find that our approach provides
significantly better prediction accuracy results than various
baseline algorithms for all evaluated user groups, i.e., (i) lowmainstream music listeners, (ii) medium-mainstream music
listeners, and (iii) high-mainstream music listeners. Furthermore, our approach is based on a simple psychological model,
which contributes to the transparency and explainability of
the calculated predictions.