【摘要】 网络大数据之间独立性较强,导致信息推荐难度较大,无法满足用户个性化要求。提出基于知识图谱的智能信息推荐模型。扩展与改进单元模型,设计领域知识模型,并动态更新知识图谱,设计学习者模型架构。利用牛顿-拉夫逊迭代法求取认知水平,界定信息推荐达成度,实现最优路径的智能信息推荐。仿真结果证明,所构建的模型信息推荐路径更优,且推荐精度更高,具有更高的实际应用价值。更多还原

【Abstract】 The strong independence among the network big data cannot meet the personalized requirements of users. Therefore, this paper proposes an intelligent information recommendation model based on knowledge graph. After the unit model was extended and improved, the domain knowledge model was designed. Simultaneously, the knowledge graph was dynamically updated to design the learner model architecture. Newton Raphson iterative method was adopted to obtain the cognitive level, and the degree of information recommendation achievement was defined, and eventually, intelligent information recommendation of the optimal path was completed. The simulation results show that the model constructed in this paper has better information recommendation path, high recommendation accuracy and practical application value.更多还原