An Improved Recommendation Algorithm in Knowledge Network

Abstract

Knowledge sharing is an essential characteristic of knowledge network and knowledge recommendation is a good way to improve the quality of shared knowledge. To investigate more efficient knowledge recommendation algorithm, knowledge potential energy rank and interest measure of knowledge were added in the association rules. Augmentation and quantization of the two parameters is the most important innovation of the paper. There were three modules in the algorithm-mining knowledge association rules, recommending related knowledge and modifying knowledge rank. Examination process provided by coal-wz.com was used to verify the proposed approach. The results showed that the proposed approach not only is able to extract the rules more efficient and much faster, but also can discover association rules more accurate in the context of knowledge management in knowledge network.