Research map / Label-efficient and robust learning / Recognition and long-tail
Collaborative Filtering With User-Item Co-Autoregressive Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use chronological orderings rather than all possible orderings (ablation) | CF-UIcA · MovieLens 1M | HR@10: 0.692 -> 0.736 | Helped |
| use chronological orderings rather than all possible orderings (ablation) | CF-UIcA · MovieLens 1M | NDCG@10: 0.406 -> 0.492 | Helped |
| use chronological orderings rather than reversed chronological orderings (ablation) | CF-UIcA · MovieLens 1M | HR@10: 0.616 -> 0.736 | Helped |
| use chronological orderings rather than reversed chronological orderings (ablation) | CF-UIcA · MovieLens 1M | NDCG@10: 0.353 -> 0.492 | Helped |
| increase the number of user clusters (ablation) | CF-UIcA · Netflix | RMSE | Helped |
| increase hidden units from a small capacity (ablation) | CF-UIcA · MovieLens 1M | RMSE | Mixed |
Closest papers by meaning
- A Neural Autoregressive Approach to Collaborative Filtering (ICML 2016 · similarity 0.93)
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- Towards Faster Deep Collaborative Filtering via Hierarchical Decision Networks (AAAI 2021 · similarity 0.87)
- Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation (AAAI 2020 · similarity 0.86)
- Auto Encoding Neural Process for Multi-interest Recommendation (AAAI 2025 · similarity 0.85)
- TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation (NeurIPS 2025 · similarity 0.84)
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