Development of a knowledge-based system based on Collaborative-Filtering recommendation for training knowledge sharing

Knowledge sharing has been an important issue to the outcomes of knowledge management. However, traditional knowledge management systems only focus on seeking how to store the knowledge, as documents, which has been retained by enterprises while ignoring the core advantages of knowledge sharing such knowledge as the experiences, technical capabilities and organization culture among employees. Therefore, how to support employees retrieve what knowledge they require for improving their work efficiency is an important issue. This study aims to developing a knowledge-based system for training knowledge sharing and management. Firstly, we apply Case-Based Reasoning (CBR) approach to support enterprises in obtaining and managing case knowledge, and promote knowledge sharing and accumulation by developing personal services and interactive functions based on Web 2.0. Especially, the system allows the knowledge to be more effectively communicated, shared and exchanged among users by integrating the Collaborative-Filtering recommender, which automatically produces and recommends knowledge-based on users' preferences and requirements.