Applying domain knowledge and social information to product analysis and recommendations: an agent-based decision support system

Abstract: The advance of Internet and Web technologies has boosted the development of electronic commerce. More and more people have changed their traditional trading behaviors and started to conduct Internet shopping. However, the exponentially increasing product information provided by Internet enterprises causes the problem of information overload, and this inevitably reduces the customer's satisfaction and loyalty. To overcome this problem, in this paper we propose a multi‐agent system that is capable of eliciting expert knowledge and of recommending optimal products for individual consumers. The recommendations are based on both product knowledge from domain experts and the customer's preferences from system–consumer interactions. In addition, the system also uses behavior patterns collected from previous consumers to predict what the current consumer may expect. Experiments have been conducted and the results show that our system can give sensible recommendations, and it is able to adapt to the most up‐to‐date preferences for the customers.     

A product configuration analysis method for emotional design using a personal construct theory

Identifying emotion-related product attributes (perceived by consumers) is no easy task in the realm of emotional design. Conventionally, this process relies heavily on the researchers who conduct the Kansei experiments selecting product attributes such as color, form, and texture for Kansei studies. However, in so doing, other product attributes that also play a vital role in product-emotion associations might be neglected by the researchers. More importantly, the identification of product attributes should be based on consumer's point of view (and feelings). Accordingly, a personal construct theory based product configuration analysis method is proposed in this work. The method develops the customer's mind map for each Kansei tag in order to capture replications of candidate products. A means-value chain is used to generate targets which are later compared with candidate products by consumers. The comparison results could suggest product attributes that are relevant to the desired Kansei. The proposed approach is presented and illustrated using a case study of Graffiti designs on notebooks. Results obtained are discussed. It appears that the proposed method is promising in identifying product attributes with desired Kansei impacts.     

Applying fuzzy multiple attributes decision making for product configuration

Mass customization (MC) is an emergent concept in industry intended to provide customized products through flexible processes in high volumes and at reasonably low costs. The method of configuration is one of important ways to realize quickly product customization. But, in business, particularly through the Internet, a customer normally develops in his mind some sort of ambiguity, given the choice of similar alternative products. This paper proposes a new approach to product configuration by applying the theory of fuzzy multiple attribute decision making (FMADM), which focus on uncertain and fuzzy requirements the customer submits to the product supplier. The proposed method can be used either in the product data management system or e-commerce websites, with which it is easy for customers to get his preferred product according to the utility value with respect to all attributes. Finally, the digital camera is taken as an example to further verify the validity and the feasibility of the proposed method.     

An investigation into affective design using sorting technique and Kohonen self-organising map

In this paper, a prototype system that focuses on affective design perspective for iterative product concept development is proposed and described. The prototype system, which emphasises the solicitation of affective attributes from customers, employs a sorting technique, i.e. picture sorts, for acquiring customer's affective requirements and a hierarchical structure for representing designer's formal elements to meet customer's affective requirements in product conceptualisation. As hierarchical structure alone contains qualitative and uncertain inherence, a self-organised algorithm known as Kohonen self-organising map (SOM) neural network is employed to consolidate the relationship between affective requirements from customers and formal elements from designers so as to formulate a customer-oriented product concept. The performance of the prototype system is illustrated by using a case study on the design of a mobile hand phone.     

Partitioned fuzzy integral nested logit model for the Taiwan's internet telephony market

The multinomial logit (MNL) model is the most widely used discrete choice model. It assumes theindependence of irrelevant alternatives (IIA) and the independence of attributes. The nested logit (NL) model has proven to be the most successful in solving IIA problem. However, the attributes of the NL model remain independent while attributes of social problems are often interdependent. Some researchers suggest using a fuzzy integral, and applying the idea of a fuzzy measure to solve the non-additive problem. Yet this method is unsuitable for stated preference data, which is based on hypothetical choice data. Factor analysis, the Choquet integral, and the NL model combine in this study using the partitioned fuzzy integral nested logit (PFINL) model as an innovative way to solve these problems. Taiwan's Internet Telephony market illustrates this model, and shows that the PFINL model is superior to the MNL and the NL model.     

Fuzzy quality function deployment: determining the distributions of effort dedicated to technical change

A model is presented that develops the Quality Function Deployment (QFD) House of Quality tool into a fuzzy‐set based multi‐criteria decision‐making process to determine the distributions of effort directed toward technical changes. When customers are polled for desired product‐specific attributes, the responses are typically defined by linguistic variables that represent a fuzzy set distribution. Fuzzy sets also define customer perceptions of the product attributes and technical expert opinions about product design criteria relative to marketplace competitors. Therefore, for each technical criterion, the following factors have an effect on the decision to implement change(s): (1) fuzzy sets providing evidence of customer need for attributes; (2) motivation to change to satisfy desired customer attributes; (3) motivation for technical criterion change; and (4) the strength of the relationships between the attributes and the technical criterion. Priority rankings are linked to the distributions of effort to apply in fulfilling continuous product improvement.     

基于模糊加权相似度量的粗糙集数据补齐方法

目前基于粗糙集的数据补齐方法,大多都是通过计算决策信息系统中具有缺失值的对象与无缺失值的对象之间的相似性,选取相似性最大的对象的属性值来补齐缺失的数据。这类算法的问题在于：计算对象之间的相似性时所有条件属性对于决策属性的重要性是相同的,忽略了条件属性间的差异性。鉴于此,引入了模糊加权相似的概念,根据每个条件属性的重要性以及决策属性对条件属性的依赖度,计算对象间的相似性,提出基于模糊加权相似性度量的粗糙集数据补齐方法,并通过实例计算以及与现有算法的比较分析,说明了方法的有效性。     

Product classification in the Internet business—a fuzzy approach

In this paper, a methodology has been introduced as a decision support tool to the consumers in the Internet business. This decision support tool takes into account the multiple attributes of the product, analyses them with respect to the consumer's desire, and finally classifies these products into different hierarchical levels as per the consumer's level of preference. The product attributes, which are in general conflicting, imprecise, and non-commensurable in nature, are well handled here by using the concepts of fuzzy logic. Concepts of linguistic quantifier are used to quantify the qualitatively defined items and also to classify the products into different preference levels as required by the customer. Classification of the products into preference levels in any business, particularly, in the business through the Internet, gives a boost to the customer and helps him in a final product choice. The procedure described here can be used by virtual buying agents for generating a hierarchical classification based on buyer's preference. At the end, a numerical example is illustrated to highlight the procedure.     

一种加权的空间模糊动态聚类算法

一般空间模糊聚类算法没有区分各属性之间的不平衡性和讨论分类数何时为最佳,针对这一问题,提出了一种加权空间模糊动态聚类算法。该算法首先利用层次分析法得到各属性的权值;然后将权值与空间模糊动态聚类法相结合;最后利用概率统计中的F-分布来确定最佳分类,以提高空间模糊聚类算法的智能性。将文中算法与基于模糊等价关系的传递闭包方法进行比较,试验表明,该算法聚类准确率要明显高于未加权的模糊聚类算法。     

Multi‐Criteria Usability Evaluation of Electronic Devices in a Fuzzy Environment

The user‐oriented design is the most important competition issue in today's market. In recent years, user‐oriented design (i.e., usability) has become expected, particularly in electronic devices. Usability considers high user satisfaction in conjunction with user performance. Usability of electronic products is essential for high customer satisfaction as well as product life cycles in companies. Therefore, MP3 players were selected among the electronic products. Designing usable MP3 players is extremely important for users who have close interaction with them. In this study, MP3 players of different types and sizes were selected and their usability evaluated. The usability criteria used in the MP3 players' evaluation were divided into two major categories: performance and emotional expectations. The alternatives were ranked with fuzzy multi‐criteria decision‐making methods, namely, Fuzzy TOPSIS (FTOPSIS), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Axiomatic Design Theory (FADT), and the results were compared. Although the same decision matrices and data obtained from semantic differential experiment were used for all fuzzy multi‐criteria decision‐making methods, it is shown that different rankings were obtained. © 2012 Wiley Periodicals, Inc.     

直觉模糊语言信息下的三维动态评价方法及应用

针对当前综合评价大多集中在静态评价以及属性权重未知的问题,在直觉模糊语言环境下提出一种三维动态评价方法.首先,利用模糊质量功能展开求解指标权重以弥补传统主客观确权方法的不足,体现指标越能表征顾客需求贡献度权重越大的赋权思路;其次,考虑到评价指标间的相互关系也会影响最终的评价结果,引入幂平均算子并将其扩展到直觉模糊语言集中,通过直觉模糊语言幂加权平均算子集结多属性信息得到静态综合评价值;接着,在静态评价值求解的基础上,基于全局信息定义分层激励因子和阶段信息定义成长因子分别对评价基础值的增长态势和增长趋势进行测度,构建基于评价基础值、变化状态与发展趋势的三维动态评价指数模型;最后以小微企业的信用评价为例验证所提出方法的有效性和可行性.     

A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction

Due to the continuous release of new products, manufacturers are paying attention to customer-oriented design of products that meet user needs to minimize the risk of their products being rejected by the market. Due to the ambiguity of user cognition, it is difficult to accurately obtain the user's preference for individual productions. To respond to the challenge, we propose an engineering scientific research method of interactive genetic algorithm with the interval arithmetic based on hesitation and fuzzy kano model(FKM) to explore the emotional needs of users for product forms and drive product modeling evolution design. Through expert interviews, the morphological characteristics and perceptual images factors of the products attracting users are investigated. In order to identify the user's satisfaction relationship with the perceptual images, we use FKM to analyze the product image style that meets the user's kansei needs accurately and selects 5 factors which is attractive attributes. Meanwhile, we attempt to transform this 5 factors into evaluation carrier to guide the evolution direction of product styling in HIIF-IGA, and then optimized four electric bikes with scores over 8.8 so that it could realize user demand-driven product evolution design. To handle users' ambiguity, the FAHP method is used to quantify the user's emotional imagery criterion and create a product evolution design system platform, which can automatically generate product styling design scheme in line with user preferences. This experimental results show that the proposed method can help enterprises effectively improve customer satisfaction and reduce the cost and time of product development.     

Heuristic and exact algorithms for product configuration in software product lines

Software product line (SPL) is a set of software applications that share a common set of features satisfying the specific needs of a particular market segment. SPL engineering is a paradigm to develop software applications that commonly use a feature model to capture and document common and variable features, and their relationships. A big challenge is to derive one product among all possible products in the SPL, which satisfies the business and customer requirements. This task is known as product configuration. Although product configuration has been extensively investigated in the literature, customer's preferences are frequently neglected. In this paper, we propose a novel approach to configure a product that considers both qualitative and quantitative feature properties. We model the product configuration task as a combinatorial optimization problem, and heuristic and exact algorithms are proposed. As far as we are concerned, this proposal is the first work in the literature that considers feature properties in both leaf and nonleaf features. Computational experiments showed that the best of our heuristics found optimal solutions for all instances where those are known. For the instances where optimal solutions are not known, our heuristic outperformed the best solution obtained by a one‐hour run of the exact algorithm by up to 67.89%.     

Applying case-based reasoning for product configuration in mass customization environments

Product variation and customization is a trend in current market-oriented manufacturing environment. Companies produce products in order to satisfy customer's needs. In the customization environment, the R&D sector in an enterprise should be able to offer differentiation in product selection after they take the order. Such product differentiation should meet the requirement of cost and manufacturing procedure. In the light of this, how to generate an accurate bill of material (BOM) that meets the customer's needs and gets ready for the production is an important issue in the intensely competitive market.The purpose of this study is to reduce effectively the time and cost of design under the premise to manufacture an accurate new product. In this study, the Case-Based Reasoning (CBR) algorithm was used to construct the new BOM. Retrieving previous cases that resemble the current problem can save a lot of time in figuring out the problem and offer a correct direction for designers. When solving a new problem, CBR technique can quickly help generate a right BOM that fits the present situation.     

Towards Autonomous and Automatic Evaluation and Negotiation in Agent-Mediated Internet Marketplaces

In an agent-mediated B2C Internet marketplace, agents, mobile or stationary, can act on behalf of customers to complete tasks involved in buying and selling products. In this paper, we first present a fuzzy-set based two-phase evaluation model allowing mobile agents to evaluate and filter online e-shops, and evaluate offers autonomously and automatically at the request from a consumer. The fuzzy evaluation criterion takes into account not only the attributes of offers, but also the reputation of corresponding e-shops. }Meanwhile, the two-phase operations can help reduce the scale of mobile agents dispatched to e-shops and thus reduce the network workload. Based on the same fuzzy evaluation criterion, an auction-like negotiation model is presented, where the consumer agent can autonomously determine the initial offer and negotiate with multiple shops simultaneously. The final best offer is determined not only by the attributes of the offers, but also the attributes of the e-shops as well as the preference the consumer specified. Finally, our models presented in this paper have been implemented in a prototype system where we conducted some experiments to evaluate their performance.     

Development of a fuzzy push delivery scheme for Internet sites

Push technology is the term recently linked with Web-based products to indicate the built-in capability of the software package to deliver relevant information to the user automatically. In this paper, a fuzzy push delivery scheme is described which 'observes' the movements of the user within the Web pages and then evaluates the personal interests and preferences of the user. With the information available from the fuzzy scheme, Web site information favoured by visitors can be 'pushed' to them without even being requested. To verify the feasibility of the concept, a Web site with various shopping spots has been developed with built-in functions to detect the time of stay in various sites by visitors . Output from the proposed fuzzy scheme is compared with the perception of the interest level given by the visitors themselves for its evaluation. The built-in feature of the scheme can significantly enhance the functionality and attractiveness of the Web site involved and hence improve its competitiveness in the global market. The development of the proposed scheme and its evaluation in an emulated Internet environment are covered in the paper.     

一种模糊多粒度用电行为异常检测方法

异常用电检测旨在识别和定位电力系统中与常规用电行为显著偏离的用户。现有基于机器学习、深度学习的有监督的检测方法通常需要大量人工标记数据,且对于离散型数据需要进行类型转换,因而容易导致重要信息的丢失。模糊粗糙集理论提供了一种处理离散数据的有效工具,并能直接应用于包含连续数据和离散数据在内的异质信息的知识分类。本文在模糊粗糙集理论的基础上提出了一个基于多粒度模糊相对差的无监督异常检测方法,并将其应用于智能电网的异常用电检测。具体而言,首先利用模糊近似空间的信息熵来度量属性在知识分类中的重要性,然后根据属性集重要性构造模糊信息粒序列,接着在此序列上定义样本的模糊相对差,最后构建基于多粒度模糊相对差的异常检测方法,并在公开的数据集上进行验证。实验结果表明了所提检测算法的有效性和优越性。实验相关的代码和数据已在网络上公开（http://www.github.com/chenbaiyang/FRAD）。     

模糊逻辑在电子商务商品推荐系统中的应用

在电子商务平台中提供有针对性的商品推荐机制以帮助用户快速准确地找到所需的商品非常重要。综合满意度是指根据用户关注的商品的不同属性的满意度在商品推荐中所占的权重不同,对商品的各属性满意度进行加权运算得到的满意度度量值。根据商品综合满意度的排序进行商品的推荐可以提高推荐的准确性。商品满意度是一个模糊的概念,提出了使用模糊逻辑来处理商品满意度的方法,并提出了一整套基于满意度的商品推荐机制。论文介绍了一个基于商品满意度的电子商务商品推荐系统的框架,重点介绍了其中的基于模糊逻辑的商品属性满意度处理方法和基于商品综合满意度的商品推荐规则,并给出了一个商品推荐的模拟实例。     

广义模糊粗糙集在模糊信息约简中的应用

针对数据集为模糊值时冗余信息难于消除的问题,提出了基于模糊相似关系的广义模糊粗糙集与QuickReduct算法相结合的方法。利用广义模糊粗糙集数据相似程度对属性值为实数值的数据集合进行约简,不需要预先对原始数据集合进行离散化,约简结果能更完整地反映原信息系统的分类能力。同时算法中利用了启发式信息,使模糊依赖性增加较快的属性作为最小约简。计算实例验证了该方法的有效性。     

A combined fuzzy AHP-simulation approach to CAD software selection

In this paper, a combined approach, where the fuzzy analytic hierarchy process (AHP) and simulation come together, is presented to select the best computer-aided design (CAD) software out of the available options in the market. The fuzzy AHP is used due to the vagueness and uncertainty of the judgements of a decision maker(s), because the crisp pair-wise comparison in the conventional AHP seems to be insufficient and imprecise to capture the right judgements of the decision maker(s). In this study, first the fuzzy AHP is used to reduce a possible number of alternatives for the CAD system to an acceptable level for further study, simulation analysis. Secondly, a simulation generator as an integrated part of the fuzzy AHP is used to try the remaining alternatives, on the generated model of a real-life product organisation in which the final alternative will be used. The results of simulation experiments are obtained, and then evaluated to reach to the ultimate CAD alternative.