文献类型：期刊文献

中文题名：Research on Recommendation Algorithms Based on Cloud Models in Probabilistic Linguistic Environments

作者：Peng YANG[1];Xifeng MA[1];Meng WEI[1];Chunsheng CUI[1];Libin CHE[1]

第一作者：Peng YANG

机构：[1]College of Computer and Information Engineering,Henan University of Economics and Law,Zhengzhou 450046,China

第一机构：河南财经政法大学计算机与信息工程学院

年份：2024

卷号：12

期号：1

起止页码：96-112

中文期刊名：Journal of Systems Science and Information

外文期刊名：系统科学与信息学报（英文）

收录：Scopus;CSCD:【CSCD2023_2024】；

基金：Supported by the Humanities and Social Sciences Research Planning Fund Project of the Ministry of Education(23YJA860004);the Major Basic Research Project of Philosophy and Social Sciences in Higher Education Institutions in Henan Province(2024-JCZD-27);2021 Project of Huamao Financial Research Institute of Henan University of Economics and Law(HCHM-2021YB001)。

语种：英文

中文关键词：recommendation algorithm;cloud model;probabilistic linguistic term set;text reviews

摘要：To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language environment.Initially,this paper quantifies the attributes in the review text based on the probabilistic linguistic term set.Subsequently,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud generator.Finally,the products are recommended and sorted based on the digital characteristic value of the cloud model.The algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.