A semantic-expansion approach to personalized knowledge recommendation

The rapid propagation of the Internet and information technologies has changed the nature of many industries. Fast response and personalized recommendations have become natural trends for all businesses. This is particularly important for content-related products and services, such as consulting, news, and knowledge management in an organization. The digital nature of their products allows for more customized delivery over the Internet. To provide personalized services, however, a complete understanding of user profile and accurate recommendation are essential.In this paper, an Internet recommendation system that allows customized content to be suggested based on the user's browsing profile is developed. The method adopts a semantic-expansion approach to build the user profile by analyzing documents previously read by the person. Once the customer profile is constructed, personalized contents can be provided by the system. An empirical study using master theses in the National Central library in Taiwan shows that the semantic-expansion approach outperforms the traditional keyword approach in catching user interests. The proper usage of this technology can increase customer satisfaction.     

Recommendation systems for decision support: An editorial introduction

This special section is focused on recommendation systems, computer-based information systems that provide suggestions based on previous usage behavior. The papers included in this section were selected from the Pacific Asia Conference on Information Systems (PACIS) and Workshop on Electronic Business (WEB). The application domains include website browsing aids, knowledge recommendation, document clustering, and team staff selection. They provide a snapshot of current development in using recommendation systems for decision support.     

The impact of value diversity on information system development projects

Diversity of personal values among participants in an information system project is typically considered to create harmful team conflict, as do other forms of personal diversity. However, recent research establishes that information and knowledge diversity among project team members contribute to project success, leading one to question accepted thought regarding diversity of values. We model the impact of value diversity on project performance through theoretical layers of diversity, conflict, and teamwork quality. An empirical test supports hypotheses that value diversity adds to both beneficial and detrimental conflict. Project managers should compose teams with diverse project values but must control for potential detrimental effects.     

Analysis and Design of a Photovoltaic System DC Connected to the Utility With a Power Factor Corrector

This paper presents a photovoltaic (PV) system parallel connected to an electric power grid with a power factor corrector (PFC) for supplying the DC loads. The operation principles and design considerations for the presented PV system are analyzed and discussed. The balanced distribution of the power flows between the utility and the PV panels is achieved automatically by regulating the output DC voltage of the PFC. Experimental results are shown to verify the feasibility of the proposed topology, which can effectively transfer the tracked maximum power from the PV system to the DC load, while the unity power factor is obtained at the utility side.     

Internet-based selling technology and e-commerce growth: a hybrid growth theory approach with cross-model inference

Innovations associated with information and communication technology (ICT) have opened up new opportunities for the global economy and ushered in the age of e-commerce. Our objective in this research is to explore the role of Internet-based selling technology, which acts as technology infrastructure for B2C e-commerce growth at the country level. This study proposes the use of a new hybrid growth theory approach as the theoretical basis for examining exogenous and endogenous factors that influence e-commerce growth over time. We estimate three different models to evaluate their explanatory capabilities. We investigate a panel-corrected feasible generalized least squares regression that incorporates the direct effects of country-level variables. We also specify an endogeneity-adjusted two-stage least squares model and estimate it with an embedded technology adoption function in a simultaneous equation model. This permits the analysis of the relevance of IT infrastructure, as well as reverse causality between e-commerce growth and Internet-based selling technology. We test these models using archival data for four different regions, based on either 24 or 42 countries around the world, depending on the data requirements of the models. Our empirical tests evaluate quasi-production environmental inputs, in which technology determines the environment of production of e-commerce services. Our main finding is that both endogenous variables (e.g., Internet user penetration, capital invested in telecommunication) and exogenous variables (international openness) drive the GDP-normalized level of B2C e-commerce revenues over time. A second main finding is that it is useful to include an embedded technology adoption function in a modeling specification for growth, since growth is founded on the availability of relevant IT infrastructure that is made possible by the availability of venture capital. In addition, B2C e-commerce revenue growth and venture capital also contribute to the adoption of Internet-based selling technology. We discuss our overall approach for the interpretation of the strength of our main findings, as well as the policy implications, and why our hybrid growth theory approach is useful.     

ROBOT SCHEDULING IN A CIRCUIT BOARD PRODUCTION LINE:A HYBRID OR/ANN APPROACH

A hybrid method that combines human intelligence, an optimization technique (semi-Markov decision model) and an artificial neural network to solve real-time scheduling problems is proposed. The proposed method consists of three phases: data collection, optimization, and generalization. The testbed of this approach is the robot scheduling problem in a circuit board production line where one overhead robot is used to transport jobs through a line of sequential chemical process tanks. Because chemical processes are involved in this production system, any mistiming or misplacing will result in defective jobs. The proposed hybrid system performs better than the human scheduler from whom the models were formulated, both in terms of productivity and quality.     

Integrating statistical and inductive learning methods for knowledge acquisition

Inductive learning is a method for automated knowledge acquisition. It converts a set of training data into a knowledge structure. In the process of knowledge induction, statistical techniques can play a major role in improving performance. In this paper, we investigate the competition and integration between the traditional statistical and the inductive learning methods. First, the competition between these two approaches is examined. Then, a general framework for integrating these two approaches is presented. This framework suggests three possible integrations: (1) statistical methods as preprocessors for inductive learning, (2) inductive learning methods as preprocessors for statistical classification, and (3) the combination of the two methods to develop new algorithms. Finally, empirical evidence concerning these three possible integrations are discussed. The general conclusion is that algorithms integrating statistical and inductive learning concepts are likely to make the most improvement in performance.     

Effect of use contexts on the continuous use of mobile services: the case of mobile games

As mobile services become more popular and people can use them virtually anywhere, research on the effect of use contexts is gaining more attention. This research presents results from a study of continuous use of mobile services in different use contexts as defined by task and consumption place. Using mobile games as an example, the authors proposed a research model that augments current technology adoption theories to fit the hedonic nature of mobile games. The results from conducting an online survey indicate that contextual factors have significant moderating effect on the intention to play mobile games. The diverse lifestyles of users also have different attitudes and concerns regarding using mobile services. The findings suggest that service providers need to take into account the impact of use contexts and the needs of specific users when they design mobile services.     

A framework for applying intelligent agents to support electronic trading

The purpose of this paper is to study how Intelligent Agents (IAs) can be used to facilitate electronic trading. An IA is a software program designed for performing a specific task based on its own knowledge and the message it received. Given the increased complexity of Internet services, many IAs are useful to make electronic markets more effective.In the paper, activities and structures of electronic markets are reviewed and discussed with respect to the coordination mechanism and primitive activities. This is followed by an analysis of IAs useful for electronic commerce (EC). A three-layer architecture for organizing IAs for EC is developed. Finally, application of the framework to support EC and related issues are presented. The findings are useful for implementing a more effective environment for EC.