AN ONTOLOGY-BASED DOCUMENT RECOMMENDATION SYSTEM: DESIGN, IMPLEMENTATION, AND EVALUATION

With the explosion of information, more and more people are embarrassed to manage information effectively. How to search and retrieve accurate information match to people\u27s requirements has been an important issue in information management research. Although search engine can solve this problem partly, the support of manage information is still limited. To use search engine, the users should input precise keywords by themselves first and this stage might cause much confusion to users. For that reason, we need a recommendation system that can catch users\u27 preferences to help users to obtain information more quickly and conveniently without copious process. In our research, a recommendation system is designed based on users\u27 profile. We use ontology technology to be the core of our recommendation system, because ontology can describe the concepts and relations of individual\u27s domain knowledge. Formal Concept Analysis (FCA) algorithm is one of the most popular methods to build ontology, and we apply it to construct our experimental system to recommend master theses to subjects. In order to evaluate our recommendation system, we developed a FCA-based system and another Scoring FCA-based system as treatments, and a Keyword-based system as a control group. We focus on both users\u27 satisfaction on information quality and system quality of our systems. The results show that users have higher information satisfaction on Scoring FCA-based system and FCA-based system than Keyword-based system. This study contributes to research and practice in information recommendation system

To Design and Implement a Recommender System based on Brainwave: Applying Empirical Model Decomposition (EMD) and Neural Networks

Recommender systems collect and analyze users’ preferences to help users overcome information overload and make their decisions. In this research, we develop an online book recommender system based on users’ brainwave information. We collect users’ brainwave data by utilizing electroencephalography (EEG) device and apply empirical mode decomposition (EMD) to decompose the brainwave signals into intrinsic mode functions (IMFs). We propose a back-propagation neural networks (BPNN) model to portrait the user’s brainwave preference correlations based on IMFs of brainwave signals, thereby designing and developing the book recommender system. The experimental results show that the recommender system combined with the brainwave analysis can improve accuracy significantly. This research has highlighted a future direction for research and development on human-computer interaction (HCI) design and recommender system

Special Issue in Honor of Prof. Ting-Peng Liang’s Lifetime Contribution to the Service Innovation Discipline

This special issue is dedicated to the reminiscences of TP for his significant contributions to the global IS discipline. This PAJAIS special issue solicits research submissions that are related to the Service Innovation discipline, one of TP’s key areas of research. Since service-oriented economy is evolving into experience economy, the research topics regarding how to design products, services, information systems, and mobile services to increase users’ experience value are becoming more and more important. From a service logic perspective, innovative service design focus on how they change customer thinking, participation, and capabilities to co-create value rather than new features in order to enhance user experience. Hence, this special issue focuses on issues related to service innovation, service quality & user experience (UX)

Knowledge Creation and Firm Performance: Mediating Processes from an Organizational Agility Perspective

Knowledge creation has emerged as a critical area in information systems research in the past decade (Nonaka 1994). However, the mechanism through which knowledge creation enhances firm performance remains unclear. This paper examines the role of organization agility as a mediator between knowledge creation processes and firm performance. Our survey study of 134 firms indicates that two forms of organizational agility – customer agility and operational agility, significantly and fully mediate the effect of knowledge creation on firm performance. Our findings extend prior research by providing insights into the role of organizational agility in facilitating the effect of knowledge creation processes on firm performance. Implications for researchers and managers are discussed

Particle swarm optimization with Monte-Carlo simulation and hypothesis testing for network reliability problem

The performance of Monte-Carlo Simulation(MCS) is highly related to the number of simulation. This paper introduces a hypothesis testing technique and incorporated into a Particle Swarm Optimization(PSO) based Monte-Carlo Simulation(MCS) algorithm to solve the complex network reliability problem. The function of hypothesis testing technique is to reduce the dispensable simulation in network system reliability estimation. The proposed technique contains three components: hypothesis testing, network reliability calculation and PSO algorithm for finding solutions. The function of hypothesis testing is to abandon unpromising solutions; we use Monte-Carlo simulation to obtain network reliability; since the network reliability problem is NP-hard, PSO algorithm is applied. Since the execution time can be better decreased with the decrease of Confidence level of hypothesis testing in a range, but the solution becomes worse when the confidence level exceed a critical value, the experiment are carried out on different confidence levels for finding the critical value. The experimental results show that the proposed method can reduce the computational cost without any loss of its performance under a certain confidence level

Segment-based predominant learning swarm optimizer for large-scale optimization

Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified