Concerning data sparseness,low accuracy and poor real-time performance of traditional collaborative filtering recommendation algorithm in e-commerce system under the background of big data,a new collaborative filtering recommendation algorithm based on improved clustering and matrix decomposition was proposed.Firstly,the dimensionality reduction and data filling of the original data were reliazed by matrix decomposition.Then the time decay function was introduced to deal with user score.The attribute vector of a project was used to characterize the project and the interest vector of user was used to characterize the user,then the projects and users were clustered by k-means clustering algorithm.By using the improved similaritymeasure method,the nearest neighbors and the project recommendation candidate set in the cluster were searched,thus the recommendation was made.Experimental results show that the proposed algorithm can not only solve the problem of sparse data and cold start caused by new projects,but also can reflect the change of user's interest in multidimension,and the accuracy of recommendation algorithm is obviously improved.%大数据背景下,对于传统的协同过滤推荐算法在电子商务系统中的数据稀疏性、准确性不高、实时性不足等问题,提出一种改进的协同过滤推荐算法.该算法首先通过矩阵分解实现对原始数据的降维及其数据填充,并引入了时间衰减函数预处理用户评分,用项目的属性向量来表征项目,用用户的兴趣向量来表征用户,通过k-means聚类算法对用户和项目分别进行聚类;然后使用改进相似性度量方法在簇中查找用户的最近邻和项目推荐候选集,产生推荐.实验结果表明,该算法不仅可以有效解决数据稀疏和新项目带来的冷启动问题,而且还可以在多维度下反映用户的兴趣变化,推荐算法的准确度明显提升.
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