A User Preference Classification Method in Information Recommendation System

As information overload problem more serious on the Internet, it becomes an important issue for users to retrieve information effectively. An information recommendation system is helpful for providing user information meet he/she requirements appropriately. However the traditional recommendation concepts usual classify a user into one preference class. It seems unreasonable because a user may possess interests about many information classes generally. In this study we propose a new recommendation concept in information recommendation system, namely club member, differs from content-based and collaboration filter method. It can classify a user into some clubs which he/she interests with different preference degrees. In order to classify users into multi-club with different preference degrees, fuzzy association rule based on data mining technology is applied in this study. Fuzzy association rules are generated by discovering and analyzing the members’ feature in the same club. According to fuzzy association rules, a new user can be classified into some clubs that he/she may interest with preference degrees. It is helpful for an information recommendation system to provide user more suitable information in accordance with their preferences precisely

A Knowledge Management performance evaluation model based on fuzzy set theory

As the knowledge-based economy time comes, the core of business process is transforming financial intensive into technology intensive and knowledge intensive gradually. However, the value of knowledge itself can’t be measured easily. We must evaluate and investigate the performance of knowledge management through activities of knowledge management process. During the performance evaluation process, many uncertain factors must be considered. It is also involved ambiguity occurred by human subjective judgment. Therefore, a performance evaluation model of knowledge management is proposed in this paper by combining Fuzzy Delphi with Fuzzy AHP. Finally, a numerical example is given to demonstrate the procedure for the proposed method at the end of this paper

Applying fuzzy PERT in cycle time management for supply chain system

As the fast technology evolution and the globalization trend, the competition among businesses transform into supply chain network gradually. However, the operation performance of the supply chain network influenced by many uncertain factors. These uncertain influence factors include the demand of the customer, the response time of delivery, operation lead time, stock level and cost. Under foregoing conditions, enterprises cannot control the variations in actual environment precisely and satisfy customers’ requirements. Therefore, the linguistic variables are used to express these uncertainties in this paper. First, this paper presents a procedure to transform the operation processes of the supply chain network into a diagram to indicate the activities among members of supply chain. Second, combining fuzzy set theory with the program evaluation and review technique (PERT) to estimate the cycle time of supply chain system. According to the fuzzy PERT model, we can calculate the cycle time and find out the critical path of a supply chain system. Furthermore, we can compute the possibility of the order fulfillment of a supply chain system. Finally, a numerical simulation is presented to illustrate the procedure of this proposed model

A design of Personal Information Push-Delivery System on the Internet

The Internet provides a powerful disseminative ability for users to acquire information more efficiently and quickly. However, an increasingly large scale of data induces certain problems as users face a more serious information overload situation. By using an information retrieval technique, information push-delivery provides a good solution for users to acquire rich information from the Internet. In fact, providing personal service for users is one of the mo st important issues in an electronic commerce (EC) environment. In order to increase interaction between themselves and customers, many enterprises provide personal services to improve management performance and competitiveness. However, since the customers have different preferences for information received from the Internet, it seems necessary to design a personal information system to guarantee that the customers can receive the desired information. In this study, the fuzzy retrieval and similarity measurement techniques are applied to design a personal information push-delivery system. The data resulting from testing a group of students at Da-Yeh University, Changhua, Taiwan, shows that the satisfaction degree for the received information for all participants was 70%. These results indicate that the proposed system can effectively provide correct and interesting information to users

A user preference perception model using data mining on a Web-based Environment

In a competitive environment, how to provide the information and products to meet the requirements of customers and improve the customer satisfaction will be the key criteria to measure a company’s competitiveness. Customer Relationship Management (CRM) becomes an important issue in any business market gradually. Using information technology, businesses can achieve their requirements for one to one marketing more efficiently with lower cost, labor and time. In this paper, we proposed a user preference perception model by using data mining technology on a web-based environment. First, the users’ web browse records are aggregated. Second, fuzzy set theory and most sequential pattern mining algorithm are used to infer the users’ preference changes in a period. After the test had processed, we use the on-line questionnaire to investigate the customer satisfaction degree from all participators. The results show that the degree of satisfaction was up to 72% for receiving the new information of participants whose preferences had been changed. It indicates that the proposed system can effectively perceive the change of preference for users on a web environment

Sponge prokaryote communities in Taiwanese coral reef and shallow hydrothermal vent ecosystems

Previously, it was believed that the prokaryote communities of typical ‘low-microbial abundance’ (LMA) or ‘non-symbiont harboring’ sponges were merely subsets of the prokaryote plankton community. Recent research has, however, shown that these sponges are dominated by particular clades of Proteobacteria or Cyanobacteria. Here, we expand on this research and assess the composition and putative functional profiles of prokaryotic communities from LMA sponges collected in two ecosystems (coral reef and hydrothermal vent) from vicinal islands of Taiwan with distinct physicochemical conditions. Six sponge species identified as Acanthella cavernosa (Bubarida), Echinodictyum asperum, Ptilocaulis spiculifer (Axinellida), Jaspis splendens (Tetractinellida), Stylissa carteri (Scopalinida) and Suberites sp. (Suberitida) were sampled in coral reefs in the Penghu archipelago. One sponge species provisionally identified as Hymeniacidon novo spec. (Suberitida) was sampled in hydrothermal vent habitat. Each sponge was dominated by a limited set of operational taxonomic units which were similar to sequences from organisms previously obtained from other LMA sponges. There was a distinct bacterial community between sponges collected in coral reef and in hydrothermal vents. The putative functional profile revealed that the prokaryote community from sponges collected in hydrothermal vents was significantly enriched for pathways related to DNA replication and repair.publishe