基于灰色关联聚类的协同过滤推荐算法

针对协同过滤推荐系统具有数据的高稀疏,高维度,数据量大的特点,本文将灰色关联聚类与协同过虑推荐算法相结合,构建了灰色关联聚类的协同过滤推荐算法,将其应用到协同过滤推荐系统中,以解决数据具有高稀疏高维度的特性情况下的个性化推荐质量问题。首先,定义了推荐系统中的用户项目评分矩阵,用户灰色绝对关联度,用户灰色相似度,用户灰色关联聚类。然后,给出了灰色关联聚类的协同过滤推荐算法的计算方法和步骤,同时给出了评价推荐质量方法。最后,将本文算法与基于余弦,相关分析及修正的余弦等协同过滤推荐算法在大小不同的数据集下进行了实验,实验表明灰色关联聚类的协同过滤推荐算法相较于传统的协同过滤推荐方法具有推荐质量高,计算量小,对数据大小要求不高等优点,同时在推荐系统的冷启动,稳定性和计算效率方面也具有一定的优势。     

个性化推荐系统中协同过滤推荐算法优化研究

协同过滤推荐算法是目前个性化推荐系统中应用比较广泛的一种算法。然而,它在处理数据稀疏性、可扩展性等方面存在一定不足。针对数据稀疏性问题,本文首先基于Slope One算法对初始的评分矩阵进行缺失值填充,其次利用基于K-means聚类的协同过滤算法预测目标用户的评分,并结合MovieLens数据集给出了相关对比实验;针对扩展性问题,本文首先提出了一种基于中心聚集参数的改进K-means算法,其次,给出了基于中心聚集参数改进K-means的协同过滤推荐算法流程,并结合MovieLens数据集设计了相关对比实验。实验结果表明,本文所提方法推荐精度均得到显著提高,数据稀疏性和扩展性问题得到了有效改善。因此,本文的研究结论不仅可进一步丰富协同过滤推荐算法的现有理论成果,还可以为提高推荐系统的精度提供理论依据和决策参考。     

数据稀疏环境下基于用户主题偏好的协同过滤算法

在B2C电子商务中,user-based协同过滤算法是一种重要的推荐方法,但用户共同评价项目数据稀疏影响了user-based协同过滤算法的应用。鉴于此,在考虑用户消费水平的基础上,利用关联规则挖掘形式化描述商品间的替代相似性;利用基于时间的贝叶斯概率描述商品间的关联关系构建商品网络,通过社会网络分析中的成份分析方法对商品网分析,得到面向用户主题偏好的商品间互补性关系,进而利用这两种商品间关系构建用户主题偏好项目集,最后在数据极度稀疏的环境下通过F₁方法和多样性测量方法与传统推荐算法进行对比实验分析,实验结果显示提高了推荐结果的准确性与新颖性。研究用的所有数据均采集于京东商城网站。本文为缓解数据稀疏问题提出了一种新的方法,扩展了整体网分析方法在商品关系分析中的应用,含有理论与实践双重意义。     

不同推荐系统输入的聚类实现

论文在分析推荐输入瓶颈问题的基础上,借助社区思想实现了显式评分输入的用户聚类,解决了评分矩阵稀疏的问题;借助用户兴趣度的定义,实现了隐式浏览输入的用户聚类,解决了用户兴趣度不易获取的问题．论文的研究立足于推荐系统的输入,通过聚类分析,为推荐算法的研究奠定了理论基础．     

融入项目类别信息的协同过滤推荐算法

协同过滤技术在电子商务领域得到了广泛的研究和应用,但是随着互联网的迅速普及和电子商务网站规模的急剧增长,用户评分的极端稀疏性导致协同过滤方法的推荐质量不高.提出了一种融入项目类别信息的协同过滤推荐算法,结合项目的类别信息为活动用户筛选出候选近邻集合,在候选近邻集合内综合利用项目的评分信息和类别信息对未评分值进行预测,最后依据用户实际评分和预测评分计算出活动用户的最近邻集合并进行推荐.实验结果表明,该算法具有较好的推荐准确性和实时性.     

基于迭代加权回归的推荐算法

评分预测问题是推荐系统研究的核心.本文利用用户评分数据集发掘商品之间的自相关性:将商品看作数据网络中的节点,用商品间的差异度定义节点间的距离,进而将评分预测问题转化为网络回归问题.然后使用迭代加权回归算法进行评分预测.通过对电影评分数据集Movie Lens的分析,验证了算法的有效性,结果表明迭代加权回归算法优于基于项目邻域的协同过滤算法.     

双向聚类方法综述

传统的聚类方法由于无法提取样本和变量间的局部对应关系,并且当数据具有高维性和稀疏性时表现不佳,因此学者们提出了双向聚类,基于样本和变量间的局部关系,同时对样本和变量进行聚类,形成一系列子矩阵的聚类结果。近年来,双向聚类发展迅速,在基因分析、文本聚类、推荐系统等领域应用广泛。首先,对双向聚类方法进行梳理与归纳,重点阐述稀疏双向聚类、谱双向聚类和信息双向聚类三类方法,分析它们之间的区别和联系,并且介绍这三类方法在多源数据的整合分析、多层聚类、半监督学习以及集成学习上的发展现状和趋势;其次,重点介绍双向聚类在基因分析、文本聚类、推荐系统等领域的应用研究情况;最后,结合大数据时代的数据特征和双向聚类存在的问题,展望双向聚类未来的研究方向。     

分类稀疏低秩表示的子空间聚类方法

近年来低秩表示和稀疏表示用于子空间聚类的研究得到了广泛关注,文献中已有许多相关的子空间聚类方法.文章结合弹性网正则化低秩表示和分类稀疏表示,提出一种分类稀疏低秩表示的子空间聚类方法.方法旨在更充分地捕获数据集的局部线性结构和全局结构信息,提高聚类性能.首先采用并行分裂的自适应惩罚的线性交替方向法求解模型,然后利用求得的系数矩阵构造相似度矩阵,最后应用谱聚类方法进行聚类.另外,取代现有方法手动调节正则化参数,文章采用自适应调节正则化参数确定目标函数中各项的权重.在人工数据集、Extended Yale B数据库和CMU PIE数据库上的实验结果表明,文章方法有更明显的聚类效果和更高的准确率.     

基于位置数据的用户多周期移动行为挖掘

挖掘位置数据中的用户行为规律是大数据时代的研究热点之一.现有研究主要关注于用户在某时刻出现在某地点的行为,对于用户从一个地点移动到另一个地点的动态行为研究较为空缺.提出一种挖掘位置数据中用户移动行为的算法可以发现用户的多个周期移动行为,描述用户在时空上的移动规律.首先,利用离散傅里叶变换和自相关系数检测用户移动行为的周期,在这一过程中,利用Apriori性质减少计算复杂度;而后提出用户移动行为的生成模型,估计用户的移动行为概率矩阵,考虑到观测数据的稀疏性,采用带全局限制的动态时间规整距离对不同时间段的行为进行聚类以发现用户的多个周期移动行为.最后,我们选取某市公共自行车系统收集的位置数据进行实证分析,结果表明,新方法能有效地挖掘用户的多个周期移动行为,进一步地,通过归纳可以得到用户群体在周期移动行为上的主要特征.     

用户集聚系数对协同过滤算法的影响研究

本文研究了用户-产品二部分网络中用户集聚系数对协同过滤算法的影响.用户集聚系数是度量目标用户的所有邻居用户的特点或者兴趣爱好相同程度的一个统计量,文章将其引入协同过滤算法的相似性计算中,并提出一种改进的算法.数值模拟显示,引入用户集聚系数统计属性的改进算法相比于CF准确性可以提高12.0％,当推荐列表的长度为50时推荐列表多样性可以达到0.649,相比于经典的CF算法提高18.2％.该工作表明用户集聚系数对推荐算法具有非常大的影响,体现了个性化推荐以用户兴趣的度量为核心的基本思想.     

Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks

Recommender systems enable users to access products or articles that they would otherwise not be aware of due to the wealth of information to be found on the Internet. The two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system.In the context of artificial intelligence, Bayesian networks have been widely and successfully applied to problems with a high level of uncertainty. The field of recommendation represents a very interesting testing ground to put these probabilistic tools into practice.This paper therefore presents a new Bayesian network model to deal with the problem of hybrid recommendation by combining content-based and collaborative features. It has been tailored to the problem in hand and is equipped with a flexible topology and efficient mechanisms to estimate the required probability distributions so that probabilistic inference may be performed. The effectiveness of the model is demonstrated using the MovieLens and IMDB data sets.     

Wavelet-based Preconditioners for Dense Matrices with Non-Smooth Local Features

We present algorithms for the detection of local non-smooth features within a dense matrix and show how, by isolating such features, we are able to use wavelet compression to design preconditioners for the corresponding dense linear system. We illustrate our approach with examples from the solution of elastohydrodynamic lubrication problems and boundary integral equations.This revised version was published online in October 2005 with corrections to the Cover Date.     

融入用户社交关系与信任关系的应用推荐方法研究

随着近年来互联网技术的快速发展,应用获取平台都面临着信息过载的问题.面对大量应用,解决用户不能快速准确地找到满足其偏好的应用的问题迫在眉睫.已有的如Cosine、Pearson等协同过滤方法普遍存在稀疏性、冷启动和可扩展性等问题,从而对推荐结果产生影响.文章在考虑用户社交关系、偏好及信任关系的基础上,提出了融合用户社交...     

A preconditioning technique for Schur complement systems arising in stochastic optimization

Deterministic sample average approximations of stochastic programming problems with recourse are suitable for a scenario-based parallelization. In this paper the parallelization is obtained by using an interior-point method and a Schur complement mechanism for the interior-point linear systems. However, the direct linear solves involving the dense Schur complement matrix are expensive, and adversely affect the scalability of this approach. We address this issue by proposing a stochastic preconditioner for the Schur complement matrix and by using Krylov iterative methods for the solution of the dense linear systems. The stochastic preconditioner is built based on a subset of existing scenarios and can be assembled and factorized on a separate process before the computation of the Schur complement matrix finishes on the remaining processes. The expensive factorization of the Schur complement is removed from the parallel execution flow and the scaling of the optimization solver is considerably improved with this approach. The spectral analysis indicates an exponentially fast convergence in probability to 1 of the eigenvalues of the preconditioned matrix with the number of scenarios incorporated in the preconditioner. Numerical experiments performed on the relaxation of a unit commitment problem show good performance, in terms of both the accuracy of the solution and the execution time.     

A Newton Acceleration of the Weiszfeld Algorithm for Minimizing the Sum of Euclidean Distances

The Weiszfeld algorithm for continuous location problems can be considered as an iteratively reweighted least squares method. It generally exhibits linear convergence. In this paper, a Newton algorithm with similar simplicity is proposed to solve a continuous multifacility location problem with the Euclidean distance measure. Similar to the Weiszfeld algorithm, the main computation can be solving a weighted least squares problem at each iteration. A Cholesky factorization of a symmetric positive definite band matrix, typically with a small band width (e.g., a band width of two for a Euclidean location problem on a plane) is performed. This new algorithm can be regarded as a Newton acceleration to the Weiszfeld algorithm with fast global and local convergence. The simplicity and efficiency of the proposed algorithm makes it particularly suitable for large-scale Euclidean location problems and parallel implementation. Computational experience suggests that the proposed algorithm often performs well in the absence of the linear independence or strict complementarity assumption. In addition, the proposed algorithm is proven to be globally convergent under similar assumptions for the Weiszfeld algorithm. Although local convergence analysis is still under investigation, computation results suggest that it is typically superlinearly convergent.     

Block filtering decomposition

This paper introduces a new preconditioning technique that is suitable for matrices arising from the discretization of a system of PDEs on unstructured grids. The preconditioner satisfies a so‐called filtering property, which ensures that the input matrix is identical with the preconditioner on a given filtering vector. This vector is chosen to alleviate the effect of low‐frequency modes on convergence and so decrease or eliminate the plateau that is often observed in the convergence of iterative methods. In particular, the paper presents a general approach that allows to ensure that the filtering condition is satisfied in a matrix decomposition. The input matrix can have an arbitrary sparse structure. Hence, it can be reordered using nested dissection, to allow a parallel computation of the preconditioner and of the iterative process. We show the efficiency of our preconditioner through a set of numerical experiments on symmetric and nonsymmetric matrices. Copyright © 2014 John Wiley & Sons, Ltd.     

Accurate dense output formula for exponential integrators using the scaling and squaring method

This paper proposes an accurate dense output formula for exponential integrators. The computation of matrix exponential function is a vital step in implementing exponential integrators. By scrutinizing the computational process of matrix exponentials using the scaling and squaring method, valuable intermediate results in this process are identified and then used to establish a dense output formula. Efficient computation of dense outputs by the proposed formula enables time integration methods to set their simulation step sizes more flexibly. The efficacy of the proposed formula is verified through numerical examples from the power engineering field.     

基于用户实时行为的Slope One模型与算法

以技术创新平台为背景,针对原有协同过滤算法推荐滞后以及算法可扩展性差的问题,根据用户的实时反馈,在Slope One算法的基础上,提出了更新增量机制,分解出固定因子以及增量因子,当用户对项目的评分改变时,只需更新增量因子,提高了算法的可扩展性,更精确地反应了用户的兴趣变化。经算例验证,该算法在保证推荐精度的同时可以有效地缩短推荐时间。