基于协同过滤算法的英语资源库信息推荐模型

Information recommendation model of English resource base based on collaborative filtering algorithm

针对大数据量下的英语资源库信息推荐模型中数据稀疏性较大的问题,提出一种基于协同过滤算法的英语资源库信息推荐模型。对历史监测的数据进行深度挖掘,将原始数据通过整合完善转化为资源数据集。通过协同过滤算法和资源反馈矩阵的表述实现资源选取,根据任意用户对项目集中同一数据资源的反馈评分计算得出每个项目间的相似阈值,以此为基础拟合项目整体相似阈值,根据阈值数挑选与用户最为匹配的项目资源,完成推荐。仿真实验证明,该模型算法的推荐精准度和效率较高,有效解决了数据稀疏性较大的问题。

On the basis of the problem of large data sparsity in the English repository information recommendation model with large amount of data,an English repository information recommendation model based on collaborative filtering algorithm is proposed.The historical monitoring data are deeply mined,and the original data are transformed into resource data sets through integration and improvement.Through the expression of collaborative filtering algorithm and resource feedback matrix,the resource selection is realized.According to the feedback score of any user on the same data resource in the project set,the similarity threshold between each project is calculated.On this basis,the overall similarity threshold of the project is fitted.According to the number of thresholds,the most matching project resource with the user is selected to complete the recommendation.Simulation results show that the proposed model algorithm has high recommendation accuracy and efficiency,and effectively solves the problem of large data sparsity.