Research map / Label-efficient and robust learning / Recognition and long-tail
Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| EHCF (non-sampling, transfer, MTL) vs NMTR | heterogeneous recommendation · Taobao | HR@100: 0.1368 -> 0.2211 | Helped |
| EHCF vs NMTR | heterogeneous recommendation · Beibei | HR@100: 0.3189 -> 0.4312 | Helped |
| whole-data non-sampling training instead of negative sampling | single-behavior MF/NCF · Movielens-1M | HR@100: 0.4316 -> 0.4597 | Helped |
| add view data to purchase (ablation) | EHCF · Taobao | HR@100: 0.0936 -> 0.2061 | Helped |
| add add-to-cart data to purchase (ablation) | EHCF · Taobao | HR@100: 0.0936 -> 0.1569 | Helped |
| add transfer-based prediction layer (ablation) | EHCF · Taobao | HR@100: 0.1525 -> 0.2211 | Helped |
| add multi-task joint training (ablation) | EHCF · Taobao | HR@100: 0.1552 -> 0.2211 | Helped |
| efficient whole-data loss instead of original regression loss (ablation) | EHCF · Taobao | total training time: 640m -> 54m | Helped |
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