关键词: 知识推荐, 生成对抗网络, 情境感知, 激活扩散模型, 语义网络, 知识管理

Abstract: To solve the low-efficacy problem in leveraging universal recommendation algorithms for knowledge supply,a Context-aware GAN-based Knowledge Recommendation method (CGKR) was proposed to provide engineers with the proper knowledge.By considering the content similarity and task similarity,a knowledge relevance network was constructed for user historical ratings' semantic extending to fully explore user's interests.Based on the users' personal background information and historical behavior information,the background context and task context were constructed to get the user representation vectors contextually-detailed.To obtain the high recommendation efficacy,the Generative Adversarial Network (GAN) model was introduced,and the Context-aware GAN (CxtGAN) model using the user representation vectors with context information as collaborative training dataset was constructed to solve the sparse matrix and cold-start problems.An engineering case study was undertaken to illustrate the CGKR's effectiveness.As evidenced in the evaluation,CGKR was ensured the effectiveness of the knowledge recommended for target user in a specific task situation.

Key words: knowledge recommendation, generative adversarial network, context aware, spreading activation model, semantic network, knowledge management