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Improving One-Class Collaborative Filtering via Ranking-Based Implicit Regularizer
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add ranking-based implicit regularizer (ablation) | square-loss MF (SRRMF) · MovieLens | NDCG@200: 0.4393 -> 0.5330 (21.33%) | Helped |
| add ranking-based implicit regularizer (ablation) | square-loss MF (SRRMF) · MovieLens | Recall@200: 0.7268 -> 0.7530 (4.08%) | Helped |
| add ranking-based implicit regularizer (ablation) | square-loss MF (SRRMF) · Amazon | NDCG@200: 0.0819 -> 0.1134 (38.46%) | Helped |
| add ranking-based implicit regularizer (ablation) | square-loss MF (SRRMF) · Amazon | Recall@200: 0.2452 -> 0.2940 (19.90%) | Helped |
| add ranking-based implicit regularizer (ablation) | square-loss MF (SRRMF) · Netflix | NDCG@200: 0.3419 -> 0.4504 (31.73%) | Helped |
| add ranking-based implicit regularizer (ablation) | square-loss MF (SRRMF) · Netflix | Recall@200: 0.5829 -> 0.6067 (3.60%) | Helped |
| use SRRMF instead of WRMF (ranking regularizer vs zero-prior) | MF · MovieLens | NDCG@200: 0.4974 -> 0.5177 | Helped |
| use SRRMF instead of WRMF | MF · Amazon | NDCG@200: 0.0914 -> 0.0977 | Helped |
| use SRRMF instead of WRMF | MF · Netflix | NDCG@200: 0.4224 -> 0.4397 | Helped |
Closest papers by meaning
- gOCCF: Graph-Theoretic One-Class Collaborative Filtering Based on Uninteresting Items (AAAI 2018 · similarity 0.88)
- From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking (AAAI 2025 · similarity 0.88)
- ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering (ICLR 2024 · similarity 0.87)
- Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation (ICML 2024 · similarity 0.87)
- Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization (ICML 2025 · similarity 0.85)
- Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning (ICML 2025 · similarity 0.85)
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