Neural Metric Factorization for Recommendation
All current recommendation algorithms, when modeling user–item interactions, basically use dot product. This dot product calculation is derived from matrix factorization. We argue that an inherent drawback of matrix factorization is that latent semantic vectors of users or items sometimes do not sat...
| Päätekijät: | , , , , , |
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| Aineistotyyppi: | Artikkeli |
| Kieli: | English |
| Julkaistu: |
MDPI AG
2022-02-01
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| Sarja: | Mathematics |
| Aiheet: | |
| Linkit: | https://www.mdpi.com/2227-7390/10/3/503 |