A Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously
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Publication:6185137
DOI10.5705/ss.202021.0429MaRDI QIDQ6185137
Publication date: 29 January 2024
Published in: Statistica Sinica (Search for Journal in Brave)
Cites Work
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- Representations for partially exchangeable arrays of random variables
- Network representation using graph root distributions
- Matrix estimation by universal singular value thresholding
- Noisy low-rank matrix completion with general sampling distribution
- Exact matrix completion via convex optimization
- A Singular Value Thresholding Algorithm for Matrix Completion
- Matrix Completion under Low-Rank Missing Mechanism
- 1-Bit matrix completion
- Inference and uncertainty quantification for noisy matrix completion
- Causal Inference for Statistics, Social, and Biomedical Sciences
- The Power of Convex Relaxation: Near-Optimal Matrix Completion
- A Simpler Approach to Matrix Completion
- Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
- Random forests
- Statistical significance of the Netflix challenge
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