基于用户模糊聚类的协同过滤推荐研究

传统的协同过滤算法没有考虑用户的自身信息对评分的影响，存在的数据稀疏性、扩展性差等弊端直接影响了推荐系统的推荐质量。对此提出了一种基于用户情景模糊聚类的协同过滤推荐算法。首先根据用户情景信息利用模糊聚类算法得到情景相似的用户群分类，然后在进行协同过滤前预先通过Slope One算法填充用户一项目评分矩阵，以有效改善数据稀疏性和实时性。实验结果表明，改进后的算法在推荐精度上有较大提高。

Research on Collaborative Filtering Recommendation Based on User Fuzzy Clustering

Traditional collaborative filtering algorithm does not consider the influence caused by the users' information, and the existing issues such as data sparsity, poor scalability and others directly affecte the recommendation quality of recommendation systems. To address these issues, a collaborative filtering algorithm based on user context fuzzy clustering was proposed. First, users are clustered by fuzzy clustering algorithm according to user context, then the user-item rating matrix should be filled through slope one algorithm in advance before the traditional collaborative filtering.This effectively improves the sparsity of user rating data and the real-time performance. The experimental results indicate that the recommendation accuracy of the advanced approach is largely improved.