基于模范用户的改进协同过滤算法

针对传统协同过滤算法普遍存在的稀疏性和扩展性问题，在传统协同过滤算法的基础上提出一种基于模范用户的协同过滤算法。通过对用户空间的聚类，自动选取模范用户聚类的最优粒度，利用模范用户产生推荐。实验结果表明，与传统协同过滤算法和其他基于聚类策略的算法相比，该算法在明显提高推荐效率的同时对推荐精度和稳定性都有所改进。

Improved Collaborative Filtering Algorithm Based on Model Users

Aiming at the problem that traditional collaborative filtering algorithms generally exist highly sparse and extensibility, this paper proposes a method of virtual model of users clustering to improve collaborative filtering algorithm. By clustering users space, it gets proper clustering granular and recommendation automatically, Experimental results show that the algorithm is obviously effective, and improves prediction accuracy and stability compared with traditional collaborative filtering.