Machine learningRecommender systems
协同过滤
协同过滤通过利用众多用户的偏好来向用户推荐物品——“喜欢你所喜欢的人也喜欢这个”。它从稀疏的用户-物品交互矩阵中学习，方法是找到相似的用户或物品（邻域方法，由 Sarwar 等人在 2001 年形式化）或将矩阵分解为潜在的用户和物品因子（矩阵分解，在 Netflix 大奖赛后由 Koren 等人推广）。
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来源
- 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: 10.1145/371920.372071 ↗
- Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. DOI: 10.1109/MC.2009.263 ↗
如何引用本页
ScholarGate. (2026, June 2). Collaborative Filtering (Recommender Systems). ScholarGate. https://scholargate.app/zh/machine-learning/collaborative-filtering
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