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Online Low Rank Matrix Completion
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use OCTAL instead of UCB | online recommendation with a low-rank reward matrix · synthetic datasets, T=1000 and T=100 | regret | Helped |
| use OCTAL instead of ETC with rank-1 approximation | online recommendation · MovieLens | cumulative regret | Helped |
| use OCTAL instead of ETC with rank-1 approximation | online recommendation · Jester | cumulative regret | Helped |
| use OCTAL instead of ETC with rank-1 approximation | online recommendation · Book-Crossing | regret | Helped |
| use OCTAL instead of ETC with the best-performing exploration period | online recommendation · synthetic datasets, T=100 | regret | No clear effect |
| increase ETC exploration period (ablation) | ETC · synthetic datasets, T=1000 | regret | Mixed |
| use OCTAL's phased exploration and exploitation instead of ETC's single exploration period | online recommendation · synthetic datasets, MovieLens, Jester, and Book-Crossing | regret over time | Helped |
| use OCTAL on a reward matrix that is not well-approximated by rank 1 | online recommendation · Jester | regret | Helped |
Closest papers by meaning
- Online Matrix Completion: A Collaborative Approach with Hott Items (ICML 2024 · similarity 0.93)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.86)
- RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees (NeurIPS 2025 · similarity 0.85)
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time (ICLR 2024 · similarity 0.85)
- Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation (ICML 2024 · similarity 0.85)
- Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization (ICML 2025 · similarity 0.85)
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