摘要
概率语言术语集以其能够准确清晰地表达决策者自身偏好且能有效处理决策过程中产生的不确定信息等优点,近年来成为了推荐领域的研究热点.然而相对于个性化推荐场景,其在非个性化推荐方面的研究至今鲜有人涉及.文章将概率语言术语集的特点与非个性化推荐相结合,从各概率语言术语值之间的横向比较出发,提出了一种非个性化产品的推荐策略,以丰富非个性化推荐算法的研究.首先利用概率语言术语集描述了系统中的产品,借助数据补齐的方法建立了产品之间的可比关系.其次,以标准化的概率语言术语集为基础,构建了非个性化产品的排序矩阵.最后,运用特征向量方法得到了一般的非个性化产品的推荐排序.论文借助MovieLens数据集进行了应用,得到了有效的推荐结果.通过对比分析验证了算法的可靠性和科学性.文章研究旨在为概率语言术语集在非个性化推荐领域的应用提供一种新的思路参考.
In recent years,probabilistic linguistic term sets have become a research hotspot in the recommendation field due to their advantages of being able to accu-rately convey the preferences of decision makers and effectively deal with uncertain information in the decision-making process.However,relative to personalized recom-mendation scenarios,research on probabilistic linguistic term sets in non-personalized recommendation has seldom been involved thus far.This paper combines the fea-tures of probabilistic linguistic term sets with non-personalized recommendation and proposes a recommendation algorithm for non-personalized products based on the horizontal comparison among various probabilistic linguistic term values to enrich the research of non-personalized recommendation algorithms.First,the horizontally comparable relationship between multiattribute products is realized by completing the data of probabilistic linguistic terms.Subsequently,the standardized probabilis-tic linguistic term sets are obtained by using the standardized equation.Based on this,the ranking matrix of non-personalized products is constructed.Finally,with the solving method of eigenvalues and eigenvectors,the general recommendation ranking for non-personalized products is obtained.Taking the movie rating data in ml-latest-small in the MovieLens dataset as an example,effective recommendation results are obtained.Compared with the non-personalized recommendation method based on the vague set,the reliability and scientificity of the algorithm are verified.The purpose of this study is to provide a new idea for the application of probabilistic linguistic term sets in the field of non-personalized recommendation.
作者
崔春生
魏盟
车利斌
柳家栋
王二威
CUI Chunsheng;WEI Meng;CHE Libin;LIU Jiadong;WANG Erwei(College of Computer and Information Engineering,Henan University of Economics and Law,Zhengzhou 450046;School of Business,Beijing Institute of Technology,Zhuhai 519088)
出处
《系统科学与数学》
CSCD
北大核心
2023年第11期2990-3010,共21页
Journal of Systems Science and Mathematical Sciences
基金
2022年河南省哲学社会科学规划项目(2022BXW001)
2022年度河南省高等学校重点科研项目(22A630004)
河南财经政法大学华贸金融研究院2021年度项目
广东省普通高校重点领域专项(数字经济)旅游产品推荐算法研究(2021ZDZX3010)资助课题。
关键词
概率语言术语集
特征值
非个性化推荐
产品排序
Probabilistic linguistic term sets
eigenvalue
non-personalized recom-mendation
products ranking
