Improving top-N recommendations using batch approximation for weighted pair-wise loss
In collaborative filtering, matrix factorization and collaborative metric learning are challenged by situations where non-preferred items may appear so close to a user in the feature embedding space that they lead to degrading the recommendation performance. We call such items ‘potential impostor’ r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827023000737 |