The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service amon...The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.展开更多
With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of...With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text mining.The RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were analyzded.Chinese word segmentation was used to show the key words of different types of users and the word cloud of their using frequency.The focus of different user groups was determined to facilitate for the follow-up course recommendation and precision marketing.Experimental results showed that the improved K-Means algorithm can well depict the behavior of group users.The index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.展开更多
随着向新型能源体系的转型加速,亟待开展对多元负荷用户的复杂用能特性分析的深入研究。提出了一种综合考量电、冷、热多元负荷耦合特性的用户用能特性标签库构建技术及用户画像方法。首先运用快速相关性滤波算法剔除高冗余低相关特征,...随着向新型能源体系的转型加速,亟待开展对多元负荷用户的复杂用能特性分析的深入研究。提出了一种综合考量电、冷、热多元负荷耦合特性的用户用能特性标签库构建技术及用户画像方法。首先运用快速相关性滤波算法剔除高冗余低相关特征,并通过随机森林和递归式特征消除算法精选出具有强区分能力的用能特征。在聚类阶段,改进的自适应三支密度峰值聚类算法(three-way adaptive density peak clustering,3W-ADPC)通过结合自适应近邻搜索和三支聚类算法提升负荷聚类效果。实证结果表明,所提方法具备在计算效率和聚类精度上的双重优势,能够精准揭示多元负荷用户综合用能特性和深层次信息,证实所提方法在多元负荷用户行为研究中的实用价值。展开更多
速度和效果是聚类算法面临的两大问题.DBSCAN(density based spatial clustering of applications with noise)是典型的基于密度的一种聚类方法,对于大型数据库的聚类实验显示了它在速度上的优越性.提出了一种基于密度的递归聚类算法(re...速度和效果是聚类算法面临的两大问题.DBSCAN(density based spatial clustering of applications with noise)是典型的基于密度的一种聚类方法,对于大型数据库的聚类实验显示了它在速度上的优越性.提出了一种基于密度的递归聚类算法(recursive density based clustering algorithm,简称RDBC),此算法可以智能地、动态地修改其密度参数.RDBC是基于DBSCAN的一种改进算法,其运算复杂度和DBSCAN相同.通过在Web文档上的聚类实验,结果表明,RDBC不但保留了DBSCAN高速度的优点,而且聚类效果大大优于DBSCAN.展开更多
基金Project(61272148) supported by the National Natural Science Foundation of ChinaProject(2014FJ3122) supported by the Science and Technology Project of Hunan Province,China
文摘The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.
文摘With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user satisfaction.In this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text mining.The RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were analyzded.Chinese word segmentation was used to show the key words of different types of users and the word cloud of their using frequency.The focus of different user groups was determined to facilitate for the follow-up course recommendation and precision marketing.Experimental results showed that the improved K-Means algorithm can well depict the behavior of group users.The index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.
文摘随着向新型能源体系的转型加速,亟待开展对多元负荷用户的复杂用能特性分析的深入研究。提出了一种综合考量电、冷、热多元负荷耦合特性的用户用能特性标签库构建技术及用户画像方法。首先运用快速相关性滤波算法剔除高冗余低相关特征,并通过随机森林和递归式特征消除算法精选出具有强区分能力的用能特征。在聚类阶段,改进的自适应三支密度峰值聚类算法(three-way adaptive density peak clustering,3W-ADPC)通过结合自适应近邻搜索和三支聚类算法提升负荷聚类效果。实证结果表明,所提方法具备在计算效率和聚类精度上的双重优势,能够精准揭示多元负荷用户综合用能特性和深层次信息,证实所提方法在多元负荷用户行为研究中的实用价值。
文摘速度和效果是聚类算法面临的两大问题.DBSCAN(density based spatial clustering of applications with noise)是典型的基于密度的一种聚类方法,对于大型数据库的聚类实验显示了它在速度上的优越性.提出了一种基于密度的递归聚类算法(recursive density based clustering algorithm,简称RDBC),此算法可以智能地、动态地修改其密度参数.RDBC是基于DBSCAN的一种改进算法,其运算复杂度和DBSCAN相同.通过在Web文档上的聚类实验,结果表明,RDBC不但保留了DBSCAN高速度的优点,而且聚类效果大大优于DBSCAN.
