随着系统中资源数目和用户数目的不断增加，在整个资源空间上用户评分数据极端稀疏，给有效的查找最近邻居带来了很大的困难。 The magnitudes of items and users in the system results in the extreme sparsity of user rating data, which makes it difficult to find neighbors effectively.
节点优化编号问题是稀疏技术的关键内容之一，求其最优解比较困难。 The node ordering optimization is one of the key problems in sparsity technology and it is difficult to get its optimal solution.
与在城市获得服务，就业，休闲相比，他们则显示出人口稀疏，机遇和地理的劣势。 Associated with access to services, employment, and leisure, they are functions of population sparsity, demography, opportunity, and geography.
实验结果证明了本文方法的可行性、高效性、抗稀疏性和抗偏斜性。 The experiments show that our method is feasible, effective, sparsity -proof and skewness-proof.
针对协同过滤中的数据稀疏问题，提出了一种基于粗集的协同过滤算法。 Aiming at the problem of data sparsity for collaborative filtering, a novel rough set-based collaborative filtering algorithm is proposed.
为了降低数据稀疏性的影响，提高推荐系统的推荐生成质量，提出了一种基于多层相似性用户聚类的协同过滤推荐算法。 To overcome the difficulty of data sparsity in recommendation systems, a collaborative filtering (CF) algorithm based on clustering basal users is presented.
从SAR图像的稀疏性、噪声以及匹配追踪算法的字典三个方面出发，通过一系列的仿真实验，对本文方法性能进行了分析； The performance of the method is analysed by a series of simulation experiments based on the sparsity of SAR images, the noise and the dictionary of Match Pursuit.
本文在LMS算法及其基本变型的基础上，利用回波路径稀疏的信息，提出了相应的改进算法。 Based on the LMS algorithm and its LMS-based forms, we propose some new algorithms using the prior information of sparsity.
一个由矩量法生成的稠密矩阵经过压缩后，可以稀疏存储。 A dense matrix arising from MoM can be stored in sparsity after compressed.
这种稀疏和密集的分布错落有序，就如同聪明人设计出来的一样。 Its sparsity and spissitude is in good order, just like the arrangement by the genius.
并针对SAR图像的稀疏性，提出了整体稀疏性与局部稀疏性的概念。 Whole sparsity and local sparsity are proposed aiming at the sparsity of SAR images.
由于地质现象的复杂性和采样数据的稀疏性，仅仅利用零散的钻孔采样数据无法有效地控制最终建模结果的误差和精度。 For the complexity of geological entity and the sparsity of sample data, it is unable to control the accuracy of modeling outcome only using scattered borehole data.
除了稀疏性之外，邻域系数的相关性也可以作为先验知识加速重构算法收敛。 However, the dependency of neighborhood coefficients is also a prior to accelerate the convergence of reconstruction algorithm besides the sparsity.
在算法上采用快速逆迭代原理并应用稀疏存贮技术有效地节约了机时和内存。 The fast inverse iteration algorithm and sparsity technique are also exploided to reduce the computation time and memory requirement.
帕累托原则（也被称为重要少数法则和因素稀疏原则）指出，80%的效应来自20%的原因。 The Pareto principle (also known as the law of the vital few and the principle of factor sparsity) states that 80% of the effects comes from 20% of the causes.
为避免在进行核融合时，支持向量机稀疏性的缺失，提出将数据映射到稀疏特征空间进行研究。 In research, sample data were mapped to sparse feature space to prevent the loss of SVM's sparsity when the kernels were fused.
对高阶线性规划问题实际应用的稀疏技术的开发作了探讨。 It discusses the exploitation of sparsity in large LP problems as used in practice.
同时在目标函数中引入固定系数分量方差项，保证了图像最小重构误差和稀疏性惩罚函数之间的平衡。 At the same time, a fixed variance term of coefficients is used to yield a fixed information capacity. This term can well balance the reconstructed error and sparsity .
评分矩阵数据稀疏问题严重影响协同过滤推荐性能。 The collaborative filtering (CF) algorithms often suffer from data sparsity problem.
特别是近几十年来，农牧交错区因开垦草地、过度放牧，造成草地退化、植被稀疏、土地沙化、水土流失、沙尘风暴等一系列严重的生态问题。 Especially in the near decades, these reasons result in a series of ecological problems, such as pasture retrogression, vegetation sparsity , soil erosion and dust devil.
针对个性化推荐系统中协同过滤算法面对的数据稀疏问题，提出了一种结合用户背景信息的推荐算法。 Aiming at the difficulty of data sparsity in personalized recommendation systems, a new collaborative filtering algorithm using user background information was presented.
研究结果表明该方法在一定程度上克服了稀疏性问题和冷开始问题。 The result of this study indicate that the approach can resolve Data Sparsity problem and New-Item problem to some extent.
在高维空间中，由于数据的稀疏性，传统的聚类方法难以有效地聚类高维数据。 It is hard to cluster high-dimensional data using traditional clustering algorithm because of the sparsity of data.
然后给出了一个优化的文档频方法，并用它过滤掉一些词条以降低文本矩阵的稀疏性； And then it presented an optimized document frequency method and used this method to filter out some terms to reduce the sparsity of text matrix.
稀疏向量法通过利用向量的稀疏性来提高求解矩阵方程的效率，它被成功地应用到电力系统分析的众多问题。 The sparse vector method enhances the efficiency of matrix solution algorithms by exploiting the vector sparsity. It has been successfully applied to many problems arising in power systems.
实验结果表明即使在数据极度稀疏的情况下，改进后的算法仍然能取得较好的推荐效果。 Experiments results show that the algorithms can achieve better prediction accuracy even with extremely sparsity of data.
电子商务系统规模的日益扩大，协同过滤推荐方法也面临诸多挑战：推荐质量、可扩展性、数据稀疏性、冷开始问题等等。 But, with expansion of E-commerce system's size , collaborative filtering approach suffer from many challenges, for instance, quality of recommendations, scalability, sparsity, cold-start problem.