基于隐马尔可夫链模型的电子商务用户兴趣导航模式发现

用户智能导航模式发现已经成为电子商务领域中的一个研究热点。为此,结合电子商务站点用户网页访问时间与网页关键字信息对用户访问兴趣进行定义,借鉴经典隐马尔可夫链模型,建立用户兴趣导航模型。给出在此模型中用户兴趣导航路径的发现方法及算法描述。通过模拟数据、某B2C在线图书销售站点中的真实数据以及与经典方法的对比等方面的实验验证,结果表明：给出的模型方法能够准确、高效地找到带有用户访问兴趣的关联路径信息。这个方法可以作为一种应用于电子商务领域更为有效、实用的智能导航发现工具。     

面向电子商务的Web挖掘系统模型研究

在电子商务网站中,Web使用挖掘可以通过分析Web日志等数据源来获取与用户访问模式相关的信息。本文主要研究电子商务中的Web挖掘技术,提出一个面向电子商务站点的Web挖掘系统模型。     

Web挖掘在网站优化中的应用研究

在电子商务系统中,网站优化是改进网站设计和布局、方便用户访问站点、实现为客户个性化服务的重要手段,而Web挖掘是实现网站优化的关键技术。本文通过对Web数据的分析挖掘。提出实现确定回溯点和目标页的算法。实践证明,该算法可以有效地实现网站优化。     

Web使用挖掘在网站优化中的应用研究

针对互联网用户访问Web服务器产生的日志,结合Web使用挖掘相关理论,采用Apriori算法挖掘用户的频繁访问模式.首先进行数据预处理以保证数据的质量及提高挖掘的效率;然后对预处理后的数据采用Apriori算法进行关联规则挖掘,找出其中的频繁访问模式;最后分析结果,总结规则,提出建议.     

面向电子商务的Web使用挖掘及其应用研究

随着电子商务的深入发展,了解用户访问模式显得非常重要。从Web使用挖掘应用目标出发,综述了Web使用挖掘的数据源收集与数据预处理技术、模式发现与模式分析技术,最后介绍了Web使用挖掘在电子商务中的应用。     

电子商务中Web页面个性化推荐系统的架构

电子商务中实现个性化推荐意味着一个用户访问Web站点时能够得到个性化的服务,网站根据用户的聚类特征,向用户在线推荐一些用户可能比较感兴趣的页面。本文给出了电子商务个性化推荐的系统结构,分析了在Web日志挖掘中应用协同过滤技术,讨论了Web页面的个性化推荐过程和推荐算法。推荐算法综合考虑了用户聚类中页面的权值和用户对页面的平均评价值两个推荐因素,实现在线页面的推荐。     

Web日志挖掘中的用户浏览序列模式识别

用户浏览模式识别是现阶段Web日志挖掘的主要目标之一,研究Web日志挖掘中的序列模式识别问题,针对传统关联规则算法中阈值固定不变、大序列的数目与序列长度成反向增长的问题,对传统的关联规则算法进行改进,提出IAx算法,使长序列只需较小的支持度计数就能达到阈值,从而发现更多有意义的序列模式,同时运用理论证明该方法的正确性.     

基于Web信息挖掘的关联推荐算法研究与实现

使用Web数据挖掘技术对用户需求进行分析,其中实现Web信息个性化使用的是关联规则,这一规则能够为用户提供个性化服务,目前已成为Web应用技术的研究热点。该文分析了应用于个性化推荐的Web页面关联规则的特点,对Web数据挖掘技术常用的关联推荐算法进行探讨,内容主要涉及到Web数据挖掘技术、关联推荐算法的思路、算法分析。     

查询驱动的一种挖掘特征规则的新方法

智能查询是决策支持系统的一个重要内容 ,在数据库中挖掘特征规则是实现智能查询的一个重要手段 .目前的数据挖掘方法都是面向具体问题的 ,不能适应用户灵活的查询需要 .本文结合粗糙集理论 ,提出了一个面向查询的挖掘特征规则的新方法 ,利用该方法挖掘出的特征规则可以在精度上达到最优 .以该算法为核心 ,本文设计出一个查询特征规则的类 SQL命令 ,使用户与系统的交互问答更加方便、灵活     

基于SVM的Web日志挖掘及潜在客户发现

潜在的客户资源是商家未来的利润来源,发现了潜在的客户就可以制定相应的商业决策,并进行有针对性的客户关系管理。使用SVM方法对Web日志文件进行挖掘,以发现站点访问者中潜在客户的共同行为模式,并将其分为不同级别的目标客户群。同时,通过试验4种不同比例的训练样本,研究了非对称数据对分类结果的影响,以期获得较优的模型。     

An Empirical Investigation of Web Site Use Using a Commitment‐Based Model

Companies throughout industry are interested in retaining existing customers, because customers' continuous consumption of products and services is critical to the long‐term value propositions of most organizations. Thus, decision‐making strategies that promote continuous use and customer retention are of research interest, both theoretically and practically. In the present research, we investigate one important area of continuous usage, that of Web site use. In particular, we use several theories of commitment to understand how an individual's decision to continue to use a Web site is influenced by his or her commitment toward that Web site and the vendor that supports it. Results derived from data collected from 335 users of a variety of Web sites indicated that affective commitment, calculative commitment, quality of alternatives, and trust were significantly associated with an individual's behavioral intention to continue to use a Web site. Implications for customer retention and decision‐making strategies are discussed.     

基于混合蚁群算法的冷链物流配送路径优化研究

基于绿色物流发展理念，为企业寻求经济与环境达到双赢的局面，本研究将节能减排转化为绿色成本，融入路径优化问题中，建立以总成本最小为研究目标的冷链物流路径优化数学模型。针对蚁群算法初始阶段由于信息素不足导致收敛速度慢的问题，将A*算法与蚁群算法相结合，利用A*算法的全局收敛性和蚁群算法的正反馈性构造了一种混合蚁群算法。通过对实例进行仿真优化与对比分析，验证了模型和算法的有效性。     

基于事件链的知识导航模型研究

当前突发公共事件应急管理研究已经成为热点问题.本文从应急管理知识需求出发,研究突发事件对象要素属性关系特点,提出了基于事件链的知识导航模型.该模型由资源模型、知识本体模型、应急处置模型、事件对象模型及事件链模型组成.通过对不同模型间的关系研究,向用户提供方便、快捷、适时的知识导航功能,最后给出应用实例.     

一种差异工件单机批调度问题的蚁群优化算法

由于在利用蚁群算法构建差异工件(即工件有尺寸差异)单机批调度问题的解时,批的加工时间是不确定的.从而不能类似于经典调度问题的蚁群算法把批加工时间的倒数作为蚁群算法中的启发式信息,引入批的利用率和批的负载均衡率作为蚁群算法中的启发式信息,提出了JACO(ant colony optimization based a job sequence)和BACO(ant colony optimization based a batch sequence)两种蚁群优化算法.在算法JACO中,解的编码为工件序列,它对应着用BF(best fit)分批规则生成的调度方案,信息素代表工件间的排列顺序;在算法BACO中,解的编码为批序列,信息素代表工件间的批相关性,由此信息素通过中间信息素量来构造相应的解,并引入特定的局部优化策略,提高了算法的搜索效率.实验表明,与以往文献中的SA(simula-ted annealing)、GA(genetic algorithm)算法以及FFLPT(first-fit longest processing time)、BFLPT (best-fit longest processing time)启发式规则相比,算法JACO和BACO明显优于它们,且BACO算法比JACO算法效果更好.     

Inferring decision strategies from clickstreams in decision support systems: a new process-tracing approach using state machines

Webstores can easily gather large amounts of consumer data, including clicks on single elements of the user interface, navigation patterns, user profile data, and search texts. Such clickstream data are both interesting to merchandisers as well as to researchers in the field of decision-making behavior, because they describe consumer decision-behavior on websites. This paper introduces an approach that infers decision-behavior from clickstream data. The approach observes clicks on elements of a decision-support-system and triggers a set of state-machines for each click. Each state-machine represents a particular decision-strategy which a user can follow. The approach returns a set of decision strategies that best explain the observed click-behavior of a user. Results of two experiments show that the algorithm infers strategies accurately. In the first experiment, the approach correctly infers most of the pre-defined decision-strategies. The second study analyzes the behavior of thirty-eight respondents and finds that the inferred mix of decision-strategies fits well the behavior described in the literature to date. Results show that using decision-support-systems on a web site and observing the user’s click-behavior make it possible to infer a specific decision strategy. The proposed method is general enough to be easily applied to both research and real-world settings, along with other decision-support-systems and strategies.     

基于改进蚁群算法的低碳车辆路径问题研究

全球气候恶化危及人类生存环境,物流运输过程中产生的大量温室气体则是祸源之一。本文考虑带有碳排放约束的车辆路径问题（VRP）,以车辆行驶里程最短和碳排放量最小为目标,构建了多目标的VRP非线性规划模型。提出了一种改进的蚁群系统算法对该模型进行求解,算法在更新路径上的蚂蚁信息素时引入了混沌扰动机制,此举能降低算法运行时陷入局部最优解的概率并有效提高算法的适应性。同时,对启发因子、状态转移概率、信息素更新等环节进行了优化设计,提高了最优路径的搜索效率。最后,数值仿真实验证明了该算法的求解表现优于同类研究常用的遗传算法和禁忌搜索算法,具有较强的全局寻优能力。在灵敏性和有效性的保证下,本研究所设计的改进蚁群算法能够较好地处理低碳车辆路径问题（LCVRP）。     

Are Individual Differences Germane to the Acceptance of New Information Technologies?

Persuading users to adopt new information technologies persists as an important problem confronting those responsible for implementing new information systems. In order to better understand and manage the process of new technology adoption, several theoretical models have been proposed, of which the technology acceptance model (TAM) has gained considerable support. Beliefs and attitudes represent significant constructs in TAM. A parallel research stream suggests that individual difference factors are important in information technology acceptance but does not explicate the process by which acceptance is influenced. The objective of this paper is to clarify this process by proposing a theoretical model wherein the relationship between individual differences and IT acceptance is hypothesized to be mediated by the constructs of the technology acceptance model. In essence then, these factors are viewed as influencing an individual's beliefs about an information technology innovation; this relationship is further supported by drawing upon extensive research in learning. The theoretical model was tested in an empirical study of 230 users of an information technology innovation. Results confirm the basic structure of the model, including the mediating role of beliefs. Results also identify several individual difference variables that have significant effects on TAM's beliefs. Theoretical contributions and practical implications that follow are discussed.     

Akzeptanz anbieterseitiger Integration in die Alltagsprozesse der Konsumenten: Erste empirische Ergebnisse

The processing of product usage data represents an exceptional variety of Web 2.0. It enables the users to get real-time services via web applications in order to optimize their consumption and usage processes. The collection of usage data in real time is currently provided mainly by so-called Ambient intelligence (AmI) applications, which at the same time offer completely new options for providers with regard to the marketing of their services. In particular, the “classic” customer integration is to be expanded towards a “provider integration” (PI), in which the providers are integrated into the everyday life processes of the customers. The paper identifies the key characteristics of the PI and investigates the acceptance of PI by the development and empirical testing of an adapted acceptance model. The results of this study show that a quite extensive use of PI can be expected in the future. This will be a motivation for further research activities in this area, as outlined in the end of this paper.