Recognizing Customer Knowledge Level towards Products for Recommendation in Electronic Commerce

A powerful online recommendation system in Electronic Commerce (EC) must know its targeted customers well and employ effective marketing strategies. Market research is a very important way to know the customers well. For high-tech products with great variety such as computers, cellular phones, and digital cameras, customers’ knowledge level towards products may have a decisive influence on their purchase decision. While many online recommendation systems focus on utilizing data mining techniques in user profile and transaction data, this paper presents a method for recognizing customer knowledge level as a preprocess for more effective online recommendation in EC. The method consists of two Back Propagation Networks (BPN) and predicts based on customer characteristics and online navigation behaviors. A simple simulated digital camera EC store case study was conducted and the good preliminary result implies the good potential of the proposed method

A Framework for Enterprise Knowledge Discovery from Databases

Knowledge discovery from large databases has become an emerging research topic and application area in recent years primarily because of the successful introduction of large business information systems to enterprises in the electronic business era. However, transferring subjects/problems from managerial perspective to data mining tasks from information technology perspective requires multidisciplinary domain knowledge. This paper proposes a practical framework for enterprise knowledge discovery in a systematical manner. The six-step framework employs the cause-andeffect diagram to model enterprise processes, tasks and attributes corresponding diagram to define data mining tasks, and multi-criteria method to assess the mined results in the form of association rules. This research also applied the proposed framework to a real case study of knowledge discovery from service records. The mining results have been proven useful in product design and quality improvement and the framework has demonstrated its applicability of guiding an enterprise to discover knowledge from historical data to tackle existing problems

Use Data Mining to Improve Genetic Algorithm Efficiency for a Job Shop Scheduling Problem

This paper proposes a new improved Genetic Algorithm (GA) by utilizing a Data Mining technique, and demonstrates how it is superior to traditional GA on a popular job shop scheduling problem. GA has long been widely applied to solve complex optimization problems in a good variety of areas. It has advantages of adaptive capability, efficient search, potential to avoid local optimum, etc. In recent literature, researchers have proposed a good number of new GAs by combining basic GA with other techniques, such as heuristic rules, simulated annealing, neural networks, fuzzy sets, and so on, in order to improve the efficiency for various optimization problems. Data mining is a new evolving technology for knowledge extraction, classification, clustering, estimation, etc. The capability of finding frequent patterns in large data set is the key reason why it is integrated with GA in this research. Due to the fundamental concept of GA’s randomness during evolution, a traditional GA may become less efficient in search for optimum. By embedding the frequent schemata into the GA evolution process, the new improved GA could reduce the search time by preserving segments of good solutions without accidentally being lost due to random crossover or mutation. The proposed new GA was experimented on a popular 6x6 job shop scheduling problem. The results have shown its better efficiency than traditional GAs and potential for further research works

An On-Line Personalized Promotion Decision Support System for Electronic Commerce

With the development of the Internet and Electronic Commerce (EC), enterprises have overcome the space and time barriers and are now capable of serving customers electronically. However, it is a great challenge to attract and retain the customers over Internet. One approach is to provide the responsive personalized service to satisfy the customer demand and promote sales at the first time. Hence, in this paper, we propose a decision support system which develops best promotion products based on combinations of different marketing strategies, pricing strategies, and customer behaviors evaluated in terms of multiple criteria. Data mining techniques are utilized to help the business discover patterns to develop on-line sales promotion products for each customer for enhancing customer satisfaction and loyalty. The proposed system consists of four components: (1) establishing marketing strategies, (2) promotion pattern model, (3) personalized promotion products, and (4) on-line transaction model. A simple example is given to illustrate the implementation and application of proposed decision support system