关系数据库中带语言值权重的模糊查询*

在模糊理论的基础上,提出了将权重概念引入数据库模糊查询中,使用户对查询中各个属性的相对重视程度得以体现。为每条记录提供了一个匹配度,按匹配度的降序输出结果,方便用户选择。权重和匹配度都是语言变量［7］,其取值为语言值形式,更加贴近自然。     

The performance of recommender systems in online shopping: A user-centric study

This research investigates the effects of preference relaxation on decision-making performance of users in online preference-based product search contexts. We compare four recommender systems based on different preference relaxation methods in extensive user experiments with 111 subjects that use two real-world datasets: 1818 digital cameras and 45,278 used car advertisements gathered from popular e-commerce websites. Our results provide new insights into the positive impact of the Soft-Boundary Preference Relaxation methods on decision-making quality and effort. The paper extends previous studies on this topic and demonstrates that decision aids based on preference relaxation techniques can effectively enhance preference-based product search in online product catalogues and help alleviate common disadvantages of form-based filtering mechanisms.     

SOME PROCEDURES FOR SELECTING FUZZY SET-THEORETIC OPERATORS

Abstract

We investigate the definition of the “and” operation in multivalued logic and decision making. A set of basic properties are suggested. Rules for selecting an appropriate operator are given. A general class of connectives is also suggested.     

A recommender system to avoid customer churn: A case study

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.     

A decision support system for optimizing dynamic courier routing operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.     

基于模糊聚类的WRFM导购方法研究

提出了一种通过模糊聚类的方法将项目属性特征的相似性与项目属性权重相结合的推荐方法,首先应用AHP方法计算项目属性权重,其次运用模糊聚类技术对项目聚类,得到项目在属性特征上的相似关系群,最后,利用相似关系群综合给出推荐项目集.该方法利用Pareto的"80/20法则",在一定程度上实现了差异化对待客户,可有效改善基于项目的协同过滤推荐算法面临的对不同信誉度用户不能给出合理推荐问题,而且提高了导购系统的导购精度,减少了人工参与.     

Decision support for team staffing: An automated relational recommendation approach

Selecting individuals for teams is only rarely supported by IS. Existing systems only consider whether a person has the required technical skills and abilities for a job. Another important aspect is neglected — the match between the person and the team members in terms of interpersonal compatibility. We present a decision support system based on a relational recommendation approach for providing an automated pre-selection of candidates that fit best with future team members. The relational recommender contributes to theory by proposing an IS-supported relational approach to team staffing and to practice by offering time and cost savings for HR professionals.     

Fuzzy methods for case-based recommendation and decision support

The paper proposes two case-based methods for recommending decisions to users on the basis of information stored in a database. In both approaches, fuzzy sets and related (approximate) reasoning techniques are used for modeling user preferences and decision principles in a flexible manner. The first approach, case-based decision making, can principally be seen as a case-based counterpart to classical decision principles well-known from statistical decision theory. The second approach, called case-based elicitation, combines aspects from flexible querying of databases and case-based prediction. Roughly, imagine a user who aims at choosing an optimal alternative among a given set of options. The preferences with respect to these alternatives are formalized in terms of flexible constraints, the expression of which refers to cases stored in a database. As both types of decision support might provide useful tools for recommender systems, we also place the methods in a broader context and discuss the role of fuzzy set theory in some related fields.     

云决策分析方法

提出一种基于不确定性人工智能的决策分析方法,该方法利用云理论中的云变换和概念提升方法来刻画属性因素的概念云模型,并对每个因素都分配相应的云权重;使用X-条件云和极大判定法,找出每个因素的最佳评判语言云模型;让云权重参与综合评判,最终通过云计算得到基于云滴分布的综合评价结果,实验结果表明,与传统的模糊决策分析相比,它更具有易理解性、广普适性和高准确性等特点.     

基于语言值有序对三元组的多类型数据标准化方法

为了标准化处理多类型数据,基于语言值二元组,提出语言值有序对三元组表示模型,研究语言值有序对三元组及其性质,给出语言值有序对三元组之间的相似度.讨论语言值有序对三元组加权平均算子,构造模糊集、直觉模糊集、犹豫模糊集、语言真值直觉模糊集与语言值有序对三元组的标准化转化模型.结合语言值有序对三元组之间的相似度,将模糊集、直觉模糊集、犹豫模糊集、语言真值直觉模糊集的转化模型应用于模式识别中.最后,通过医院智能分诊的实例说明文中方法的合理性和有效性.     

模糊传感器语言概念生成实用方法

在简要介绍扩张原理基础上，讨论了模糊传感器语言概念生成的实用方法。     

A review of: “THE PARADIGM OF SELF-ORGANIZATION:” Current Trends in Self-Organization,edited by G. J. Dalenoort. Gordon and Breach,New York, 1989. xi + 332 pages

Preference Relations are a popular and powerful tool used by decision makers to provide their preference information in the process of decision making. Over the last decades, various types of preference relations have been developed, but they are scattered through the literature. The purpose of this paper is to present a comprehensive survey of preference relations. We also briefly discuss their properties and introduce some new preference relations. Finally, we put forward the directions of future research.     

基于模糊簇的个性化推荐方法

提出了一种运用模糊聚类方法将项目属性特征的相似性与协同过滤推荐算法相融合的推荐方法,此方法将用户对单个项目的偏好转化为对相似群组的偏好,目的是构造密集的用户-模糊簇的偏好信息,同时利用项目之间在相似群组的相似性来初步预测用户对未评价项目的评分,在此基础之上再完成基于用户的协同过滤推荐算法。实验结果表明,该方法确实可提高协同过滤推荐算法的推荐精度。     

Fuzzy modifiers based on fuzzy relations

In this paper we introduce a new type of fuzzy modifiers (i.e. mappings that transform a fuzzy set into a modified fuzzy set) based on fuzzy relations. We show how they can be applied for the representation of weakening adverbs (more or less, roughly) and intensifying adverbs (very, extremely) in the inclusive and the non-inclusive interpretation. We illustrate their use in an approximate reasoning scheme.     

基于重引力搜索链接预测和评分传播的大数据推荐系统

大数据推荐系统的搜索空间较大导致推荐的响应时间过长。为权衡大数据推荐系统的时间效率和推荐性能,提出一种基于重引力搜索链接预测和评分传播的大数据推荐系统。采用相对相似性指数度量用户的相似性,采用广义Meta Path模型建立相似图;引入社区信息来提高局部链接预测的准确率,从强社区提取优化的子图来实现局部链接的预测,通过重引力搜索对子图做优化处理,从而缩小搜索空间;设计基于传染病模型的网络传播策略,根据已有的模式探索隐藏的模式。基于公开数据集的实验结果表明,该算法有效地提高了推荐系统的准确率和覆盖率,并且响应时间在可接受的范围内。     

一种支持用户偏好的RDF模糊查询方法

RDF模糊查询是实现语义Web智能检索的重要组成部分,利用Zadeh的Ⅱ型模糊集合理论、α-截集及语言变量概念,提出了支持用户偏好的RDF模糊查询方法,其扩展了SPARQL语言来实现模糊及偏好表达,构造了有序语言值子域表来实现模糊值到相应子域的映射,以确定隶属度区间。利用去模糊化规则,将扩展的查询转换为标准SPARQL,利用现有的SPARQL查询引擎实现模糊查询操作。为验证提出的方法,开发了fp-SPARQL实验系统。实验结果表明,该方法提高了RDF模糊查询效率,增强了用户对查询结果的满意度。     

基于云台的变域论模糊PID算法研究

为了使云台能够快速的响应环境变化从而达到动态平衡目的,提出变域论模糊PID控制算法实现调平过程中动态特性控制设计。该系统以ARM920T为控制单元,采用模糊PID算法对云台进行控制。利用MATLAB对模糊PID算法进行了仿真分析,最后对变论域、模糊及传统算法的效果进行比较：在保证动态稳定性的前提下,模糊PID算法比常规PID算法能够有效的提高响应速度。