Research map / Learning theory and algorithms / Matrix and tensor methods
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adaptively learned community-based confidence weights instead of baseline weighting strategies | FAWMF · Movielens | Pre@5: 0.3841 -> 0.4054 | Helped |
| Use adaptively learned community-based confidence weights instead of baseline weighting strategies | FAWMF · Movielens | Rec@5: 0.0936 -> 0.0949 | Helped |
| Use adaptively learned community-based confidence weights instead of baseline weighting strategies | FAWMF · Amazon | Pre@5: 0.0988 -> 0.1129 | Helped |
| Use adaptively learned community-based confidence weights instead of baseline weighting strategies | FAWMF · Amazon | Rec@5: 0.0472 -> 0.0441 | Hurt |
| Use adaptively learned community-based confidence weights instead of baseline weighting strategies | FAWMF · Amazon | NDCG@5: 0.1060 -> 0.1285 | Helped |
| Use adaptively learned community-based confidence weights instead of baseline weighting strategies | FAWMF · Douban | Pre@5: 0.2400 -> 0.2661 | Helped |
| Use adaptively learned community-based confidence weights instead of baseline weighting strategies | FAWMF · Douban | Rec@5: 0.0666 -> 0.0680 | Helped |
| Use fBGD rather than original BGD | FAWMF · Movielens | training speed: 16 times | Helped |
| Use fBGD rather than SGD-based negative sampling | FAWMF · Movielens | Pre@5 | Helped |
| Increase latent dimension K to 60 (ablation) | FAWMF · Movielens | recommendation accuracy | Hurt |
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- Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation (ICML 2024 · similarity 0.84)
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