基于知识图谱的家政服务课程推荐融合模型

Fusion Model of Housekeeping Service Course Recommendation Based on Knowledge Graph

针对家政服务从业人员对家政服务课程在线学习需求的增加,而现有的家政服务课程在线学习网站存在资源较少、课程不够系统化和不具有课程推荐功能等状况,使得家政服务相关从业人员的在线学习门槛变高。通过分析现有的家政服务课程在线学习网站,提出构建家政服务课程知识图谱,并将家政服务课程知识图谱与推荐算法进行融合,设计了一种融合深度学习技术的规则与水波偏好传播相结合的R-RippleNet家政服务课程推荐模型。R-RippleNet模型的使用对象包括老学员和新学员,老学员部分是基于水波偏好传播模型进行课程推荐,新学员部分则基于规则模型进行课程推荐。实验结果表明,老学员使用R-RippleNet模型的AUC值为95%,ACC值为89%,F1值为89%,新学员使用R-RippleNet模型的总体精确率均值为77%,NDCG均值为93%。

Housekeeping service practitioners’demand for online learning of housekeeping service courses has increased.How-ever,the existing online learning websites of housekeeping service courses have few resources,insufficient systematic courses and no course recommendation function,which makes the threshold of online learning for housekeeping service practitioners become higher.Based on the analysis of the existing online learning websites of housekeeping service courses,this paper proposes to construct the knowledge graph of housekeeping service courses,and integrates the knowledge graph of housekeeping service courses with the recommendation algorithm,and designs an R-RippleNet recommendation model for housekeeping service courses that combines the rules of deep learning technology and the water-wave preference propagation.The objects used by R-RippleNet model include old students and new students.The old students make course recommendation based on the water wave preference propagation model,while the new students make course recommendation based on the rule model.Experimental results show that the AUC value of old trainees using R-RippleNet model is 95%,ACC value is 89%,F1 value is 89%,the mean of the overall accuracy rate of new trainees using R-RippleNet model is 77%,the mean of NDCG is 93%.