Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866911777077329920 |
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| author | Kowald, Dominik Lex, Elisabeth Schedl, Markus |
| author_facet | Kowald, Dominik Lex, Elisabeth Schedl, Markus |
| contents | 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) low-mainstream 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2003_10699 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations Kowald, Dominik Lex, Elisabeth Schedl, Markus Information Retrieval 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) low-mainstream 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. |
| title | Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2003.10699 |