基于协同过滤的电子商务推荐系统建模研究

电子商务个性化推荐系统是客户关系管理的重要内容.在分析现有电子商务推荐系统不足的基础上,设计一种基于协同过滤的智能商务推荐系统.为客户快捷便利地寻找到所需的商品信息,推荐合理的商品,实现客户与商家的共赢.     

基于协同过滤算法的高校图书馆图书推荐系统研究

基于协同过滤算法是个性化推荐系统中最基本、最常用的一种,而传统的基于用户的协同过滤算法存在着用户冷启动和高推荐率等问题。笔者针对目前图书推荐协同过滤算法运用中存在的问题,并根据高校图书馆的实际情况,提出相应的改进措施。笔者利用学生网络日志和读者借阅记录对用户进行相似性分析,有效解决了用户“冷启动”的问题,提高了推荐准确率。笔者利用时间衰减法建立用户兴趣模型,以便更好地关注用户的短期借阅行为,提高推荐准确率。与传统用户协同过滤方法相比,笔者提出了一种改进的用户协同过滤方法。结果表明,改进后的用户协同过滤方法具有更好的性能。     

电子商务个性化文档推荐技术研究

电子商务个性化文档推荐同时具有电子商务推荐系统以及文档信息处理的特点,由此本文首先概括了目前电子商务推荐以及文档信息处理的主要技术,多种推荐技术的有效集成会提高推荐的精度与个性化,也是未来电子商务中个性化文档推荐的发展趋势。其次阐述了几种重要的用户兴趣获取技术及其面临的主要问题。最后分析了电子商务个性化文档推荐技术面临的主要问题及未来发展方向,旨在挖掘用户潜在兴趣,提高文档推荐个性化和提高推荐质量。     

电子商务推荐系统中群体用户推荐问题研究

尽管传统的电子商务推荐系统在个体用户推荐方面取得了巨大成功,但它并不适用于向群体用户进行推荐。随着虚拟社区中群体用户的不断增加,构建群体推荐系统,向群体用户提供个性化推荐,减少他们搜集信息所耗费的时间和精力显得越来越重要。基于此,本文提出了一种新颖的推荐方法—结合领域专家法的群体用户推荐算法。该算法以基于项目的协同过滤技术为基础,根据群体成员间的相互作用确定群体偏好,由群体偏好产生推荐,推荐过程中存在的成员未评分项采用领域专家法进行预测填充,此外本文算法还考虑了成员间相似关系对推荐质量的影响。实验结果表明了本文算法的有效性。     

企业的协同电子商务研究

全球金融危机为电子商务的发展带来了新的契机,而协同电子商务是企业未来电子商务的发展方向。本文研究了协同电子商务的概念和含义,讨论了协同电子商务为企业带来的好处,提出了企业实施协同电子商务应注意的问题。     

团购商品的个性化协同过滤推荐方法

本文提出结合云模型和改进均方差的协同过滤推荐方法,为团购网站的用户提供个性化推荐。该方法首先通过已有的用户日志数据,模拟用户对商品的评分,构建用户-商品评分矩阵。在此基础上,通过云模型和改进MSD方法模拟商品间的相似度,运用基于物品的协同过滤方法,预测出用户对商品的评分,并为目标用户生成个性化推荐列表。最后,通过实验证明该推荐方法的有效性。     

协同过滤在CRM交叉销售中的应用研究

利用CRM数据的特殊性,将协同过滤运用到交叉销售中,期望挖掘更高质量的销售机会,提高其成功率,降低企业销售成本,更大幅度提高企业效益以及客户忠诚度。实验结果表明取得了良好效果。     

集成k-means聚类和有监督特征选择的混合式协同过滤推荐

为了提高协同过滤推荐质量,提出了集成k-means聚类和有监督特征选择的混合式协同过滤推荐框架和KDICF算法。利用有监督特征选择的方法和技术,找出与待预测项目强相关的项目集,将高维稀疏的用户-项目评分数据集转为低维用户-项目评分数据集,并运用k-means聚类,在此基础上寻找近邻用户对目标用户未评分项目进行评分预测。实验结果表明,混合式KDICF算法有着优异的性能。     

一个价值网关联的协同电子商务解构模型

从管理学角度简明了价值网的基本含义,描绘了协同电子商务的网络结构,分析了二者的关联统一性,继而探讨了价值网关联的协同电子商务解决方案及设计范式,并从理论上刻划了其解构模型,以期为联盟体中新型管理平台的再造或重构提供理想范式.     

协同运输的路线整合问题研究

本文研究协同运输的路线整合问题(CTRIP):允许所有的O-D流(运输任务)在规定的路线长度内任意采取直通运输、单点中转、两点中转的整合运输路线,整合运输的中枢路段在支付固定成本后可产生运费折扣,如何选择O-D流的整合路线使得总成本最小? CTRIP广泛应用于航空、物流、快递等领域的整合运输实践。论文构造了CTRIP的混合整数规划模型和Benders分解算法,实验显示,算法表现出非常好的计算绩效。最后,我们利用一个具体实例对CTRIP与已有研究展开了比较,结论显示CTRIP更能保证中枢路段的规模优势 。     

遗传算法求解供应链协作供应问题

本文给出一个多供应点、多产品和多需求点的供应链协作供应问题的模型,对于这样一个复杂的组合优化问题,通过设计合适的编码方案和相应的遗传算子,提出了一个改进遗传算法的求解方案,最后通过一个算例说明该方法的可行性和有效性.     

An Efficient Optimal Solution Algorithm for the Classification Problem

A new LP-based algorithm is developed to determine the optimal solution to the two-group classification problem. The procedure is efficient when compared to current research in a simulation study.     

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label “Near Rational,”“Fatalist,” and “Confused.” There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments.     

A Collaborative View of Knowledge in a Knowledge Society: An International Perspective

This paper focuses on key topics that rose to the fore at the `Getting the Story Right' Joint Forum Assembly Meeting between business and higher education leaders of Australia, Canada, Japan, Poland, and the United States which was held in October 1998 in Banff, Canada. Arising from participant observation notes, conversations, document analysis, and reflective writing, the work centers on four major themes: the nature of knowledge and knowledge workers, the fundamental difference between training and learning, the short-term effects of shifting workplace and economic situations, and the long-term impact of intergenerational influences. Each of these knowledge-related themes emerged in collaborative discussions that transcended national, occupational, and cultural boundaries. The paper takes the position that dialogue regarding the nature and usefulness of knowledge paradoxically takes an inward view as it develops outwardly to the case of others and the world. In a collaborative process of this nature, an important dimension of shared human meaning making becomes briefly revealed.     

Solving the Classification Problem in Discriminant Analysis Via Linear and Nonlinear Programming Methods*

This paper demonstrates the feasibility of applying nonlinear programming methods to solve the classification problem in discriminant analysis. The application represents a useful extension of previously proposed linear programming-based solutions for discriminant analysis. The analysis of data obtained by conducting a Monte Carlo simulation experiment shows that these new procedures are promising. Future research that should promote application of the proposed methods for solving classification problems in a business decision-making environment is discussed.     

A Collaborative Model for Organizational Transformation

The purpose of this study was to develop a collaborative learning and communication model for higher education. On a university campus there are various psycho-social and organizational barriers to collaboration inside and outside of the classrooms. Scholars have stated that teachers rarely talk with each other about their teaching, there is little multi-disciplinary research and studies have shown that there is little or no collaboration in meetings and forums outside of university classrooms. So, what are the barriers to collaborative communication and learning and how can these barriers be removed through collaborative strategies? These are the questions that this study explored. Through the understanding of the psycho-social and organizational barriers to collaboration, strategies began to unfold and a new model for collaborative learning and communication was developed. This model reflects a high synergy, totally integrated social and cognitive system for individual and organizational transformation.     

Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions

Quantitative models support investigators in several risk analysis applications. The calculation of sensitivity measures is an integral part of this analysis. However, it becomes a computationally challenging task, especially when the number of model inputs is large and the model output is spread over orders of magnitude. We introduce and test a new method for the estimation of global sensitivity measures. The new method relies on the intuition of exploiting the empirical cumulative distribution function of the simulator output. This choice allows the estimators of global sensitivity measures to be based on numbers between 0 and 1, thus fighting the curse of sparsity. For density-based sensitivity measures, we devise an approach based on moving averages that bypasses kernel-density estimation. We compare the new method to approaches for calculating popular risk analysis global sensitivity measures as well as to approaches for computing dependence measures gathering increasing interest in the machine learning and statistics literature (the Hilbert–Schmidt independence criterion and distance covariance). The comparison involves also the number of operations needed to obtain the estimates, an aspect often neglected in global sensitivity studies. We let the estimators undergo several tests, first with the wing-weight test case, then with a computationally challenging code with up to $\text{◂,▸}k=30,000$ inputs, and finally with the traditional Level E benchmark code.