面向云服务平台的用户偏好产品属性动态获取方法  

Cloud service platform oriented dynamic acquisition method for user preference product attributes

作　　者：裴卉宁 刘鑫宇 黄雪芹 谭昭芸 孙海波 白仲航[1,4] PEI Huining;LIU Xinyu;HUANG Xueqin;TAN Zhaoyun;SUN Haibo;BAI Zhonghang(School of Architecture&Art Design,Hebei University of Technology,Tianjin 300131 China;Key Laboratory of Industrial Design and Ergonomics,Ministry of Industry and Information Technology,Northwestern Polytechnical University,Xi’an 710072,China;School of Mechanical Engineering,Tianjin University,Tianjin 300000 China;National Engineering Research Center for Technological Innovation Method and Tool,Hebei University of Technology,Tianjin 300401 China)

机构地区：[1]河北工业大学建筑与艺术设计学院,天津300131 [2]西北工业大学机电学院工业设计与人机工效工信部重点实验室,陕西西安710072 [3]天津大学机械工程学院,天津300000 [4]河北工业大学国家技术创新方法与实施工具工程技术研究中心,天津300401

出　　处：《计算机集成制造系统》2023年第11期3774-3785,共12页Computer Integrated Manufacturing Systems

基　　金：河北省高等学校人文社会科学研究资助项目(SD201091);教育部人文社会科学基金资助项目(21YJCZH113)。

摘　　要：考虑云服务平台短文本数据中横向关联性与产品属性纵向层次性,提出一种层次主题模型(hLAT)用户偏好产品属性动态获取方法。首先,利用hLDA模型纵向挖掘在线文本主题层次结构,构建初始产品属性层次树;其次,考虑词语主题影响力因素以修改节点间的随机跳转概率优化TextRank算法,获取候选偏好词排序构建用户偏好产品属性主题层次树;最后,以“橙色·云工业产品协同研发”平台中“汽车工业”相关问题数据为例,验证了所提方法的可行性和有效性,为云服务平台前端建设提供了崭新构思。Considering the horizontal correlation and vertical hierarchy of product attributes in the short text data of cloud service platform,a dynamic method for acquiring product attributes of hLAT users’preference was proposed.The hierarchical Latent Dirichlet allocation(hLDA)model was used to mine the on-line text topic hierarchy and construct the initial product attribute hierarchy tree.The influence of word topic was considered to modify the random jump probability between nodes to optimize the TextRank algorithm,An example of automobile industry related problem data in the platform of“Orange Cloud Industrial Product Collaborative Research and development”was given,and the feasibility and effectiveness of the proposed method were verified,which provided a new idea for the front-end construction of cloud service platform.

关 键 词：云服务 在线文本 用户偏好 产品属性 层次主题模型 词图模型 

分 类 号：TB472[一般工业技术—工业设计] TP391.1[自动化与计算机技术—计算机应用技术]