基于改进协同过滤算法的用户页面兴趣度预测研究

根据大数据稀疏性特征，把奇异值分解方法引入协作过滤算法中进行互联网站点用户的页面兴趣度的计算和验证，提出了一种基于改进协作过滤算法的用户页面兴趣度预测算法。该算法可通过在网络日志文件中提取显性用户评分数据存在的虚假评分，发现用户页面兴趣度和其影响因素。MATLAB仿真结果显示：提出的基于改进协同过滤算法的用户页面兴趣度测量方法可有效克服海量数据的稀疏性，在预测准确性、测量速度方面都有很大提高。

Prediction of user page interest based on improved collaborative filtering algorithm

According to the sparse characteristics of big data, singular value decomposition method was introduced into collaborative fitering algorithm to calculate and verify the page interest degree of internel site users. This paper proposed a user page interest prediction algorithm based on improved collaborative filtering algorithm. The algorithm could extract the false score in the dominant user score data in the network log file, and found user page interest and influence factors. The results of MATLAB simulation show that the collaborative filtering algorithm based on singular value decomposition is accurate and efficient in predicting the interest degree of user pages under the unavoidable situation of massive data sparsity.