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基于SVD矩阵分解技术和RkNN算法的协同过滤推荐算法 被引量：1

Research on Collaborative Filtering Recommendation Algorithm Based on SVD Matrix Decomposition Technique and RkNN Algorithm
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摘要 针对现有协同过滤算法具有的可扩展性较低、数据稀疏和计算量较大缺点,提出一种基于SVD矩阵分解技术和RkNN算法的协同过滤推荐算法.本算法经SVD矩阵简化处理和kNN和RkNN的协作过滤,增强了用户的影响集,实现了测试集的未知预测评分功能.经仿真实验表明,稀疏性、可扩展性和计算量都得到有效改善,系统预测评分与用户实际评分接近,为用户提供了良好的使用体验.该算法获得了更好的预测性能,同时具有良好的可扩展性. The RBF neural network has the disadvantages of slow convergence speed and leak global searching ability, this paper presents a RBF neural network based on genetic algorithm, improves the prediction accuracy of RBF neural network prediction model and realizes nonlinear time sequence after adapting genetic operator parameters optimization. The simulation experiment results show that the prediction of RBF network prediction model is very suitable for the genetic algorithm based on nonlinear time series. It is feasible, accurate and effective.
作者 刘洋
机构地区 信阳职业技术学院数学与计算机科学学院
出处 《湖南工程学院学报（自然科学版）》 2015年第1期44-47,共4页 Journal of Hunan Institute of Engineering(Natural Science Edition)
关键词 SVD影响集 协作过滤 推荐算法 RkNN影响集 time series forecasting model RBF neural network genetic algorithm
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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共引文献19

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二级引证文献6

湖南工程学院学报（自然科学版）
2015年 第1期

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