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Job recommender is a system that automatically returns a ranked list of suitable, prospective jobs for employees. It plays a significant role in connecting employees and employers. In order to choose a suitable algorithm to build the system, a comparison study of popular recommendation methods is conducted and reported in this paper. The experimental data crawled from vietnamworks.com, itviec.com...
Job recommender systems are designed to suggest a ranked list of jobs that could be associated with employee's interest. Most of existing systems use only one approach to make recommendation for all employees, while a specific method normally is good enough for a group of employees. Therefore, this study proposes an adaptive solution to make job recommendation for different groups of user. The proposed...
Data are essential for the experiments of relevant scientific publication recommendation methods but it is difficult to build ground truth data. A naturally promising solution is using publications that are referenced by researchers to build their ground truth data. Unfortunately, this approach has not been explored in the literature, so its applicability is still a gap in our knowledge. In this research,...
Successful research collaborations may facilitate major outcomes in science and their applications. Thus, identifying effective collaborators may be a key factor that affects success. However, it is very difficult to identify potential collaborators and it is particularly difficult for young researchers who have less knowledge about other researchers and experts in their research domain. This study...
In this paper, we propose a framework to integrate bibliographical data of computer science publications from heterogeneous digital libraries. The framework consists of three key components: publication collector, bibliographical parser and duplicated checker. In order to analyze efficiency of our framework in integrating data from heterogeneous sources, we conduct experiment with three different...
To learn about the state of the art for a research project, researchers must conduct a literature survey by searching for, collecting, and reading related scientific articles. Popular search systems, online digital libraries, and Web of Science (WoS) sources such as IEEE Explorer, ACM, SpringerLink, and Google Scholar typically return results or articles that are similar to keywords in the user's...
In this paper we propose a method to extract automatically metadata (title, authors, affiliation, email, references, etc) from science papers by combining the layout information of papers with rules which are defined by using JAPE Grammar rules of GATE. After metadata extracted automatically from digital documents, user can interact and correct them before they are exported to XML files. Developing...
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