用户评分矩阵稀疏问题影响协同过滤的推荐性能。
The sparse user-item matrix often hurts the performance of recommendation system.
求解大型稀疏问题最流行的方法是建立在子空间投影技术之上的。
The most popular methods to solve large sparse problems are based on projection techniques on appropriate subspaces.
针对协同过滤中的数据稀疏问题，提出了一种基于粗集的协同过滤算法。
Aiming at the problem of data sparsity for collaborative filtering, a novel rough set-based collaborative filtering algorithm is proposed.
应用推荐