方法对比
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| 协同过滤× | 矩阵填充× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2001 | 2009 |
| 提出者≠ | GroupLens; Sarwar et al. (item-based); Koren et al. (matrix factorization) | Emmanuel Candès & Benjamin Recht |
| 类型≠ | Recommendation from user-item interactions | Convex low-rank recovery |
| 开创性文献≠ | Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, 285–295. DOI ↗ | Candès, E. J., & Recht, B. (2009). Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6), 717–772. DOI ↗ |
| 别名 | user-based collaborative filtering, item-based collaborative filtering, matrix factorization recommender, işbirlikçi filtreleme | Nuclear Norm Minimization, Collaborative Filtering via Low-Rank Recovery, Inductive Matrix Completion, Matris Tamamlama |
| 相关 | 2 | 2 |
| 摘要≠ | Collaborative filtering recommends items to a user by leveraging the preferences of many users — 'people who liked what you liked also liked this'. It learns from a sparse user-item interaction matrix, either by finding similar users or items (neighbourhood methods, formalized by Sarwar et al. in 2001) or by factorizing the matrix into latent user and item factors (matrix factorization, popularized by Koren et al. after the Netflix Prize). | Matrix Completion is a technique for recovering a low-rank matrix from a small, possibly random subset of its entries. Introduced by Emmanuel Candès and Benjamin Recht in 2009, it reformulates the problem as nuclear norm minimization — a convex surrogate for rank minimization — and provides theoretical guarantees that exact recovery is achievable when entries are observed uniformly at random and the matrix satisfies an incoherence condition. |
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