基于三支因果力的邻域推荐算法  

Neighborhood recommendation algorithm based on three⁃way causality force

作　　者：范敏 秦琴[1,2] 李金海 FAN Min;QIN Qin;LI Jinhai(Data Science Research Center,Kunming University of Science and Technology,Kunming 650500,Yunnan,China;Faculty of Science,Kunming University of Science and Technology,Kunming 650500,Yunnan,China)

机构地区：[1]昆明理工大学数据科学研究中心,云南昆明650500 [2]昆明理工大学理学院,云南昆明650500

出　　处：《山东大学学报（理学版）》2024年第5期12-22,共11页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金资助项目(11971211,12371460)。

摘　　要：本文将三支决策思想、因果力理论与形式概念分析相结合,提出了三支因果力下的邻域推荐算法。考虑到极端用户评分对推荐精度的影响,根据宽松度和严苛度对用户进行分类,修正极端用户评分。基于修正评分矩阵计算节点之间的三支余弦相似度和节点相似结构重要度,找出专家节点。在对象弱概念需要满足的目标函数和约束条件下进行聚类得到邻域,在邻域中根据属性密度识别关键的条件属性和决策属性并计算置信度,结合三支因果力提取推荐规则对社区成员进行邻域推荐。实验结果表明,本文算法的精确度、召回率、F1均优于其他传统的推荐算法。A neighborhood recommendation algorithm under three⁃way causality force by combining the three⁃way decision ideas,causal force theory,and formal concept analysis are proposed.Considering the influence of extreme user rating on recommendation accuracy,we classify users by defining the degree of leniency and severity,and correct extreme user rating.Based on the modified score matrix,the three cosine similarity and the similarity structure importance of nodes are calculated to find the expert nodes.Un⁃der the objective function and constraint conditions that the weak concept of the object needs to meet,the cluster is carried out to ob⁃tain the neighborhood,and the key conditional attributes and decision attributes are identified according to the attribute density in the neighbourhood,and the confidence between them is calculated.The three⁃way causality force extraction recommendation rules are combined to carry out neighborhood recommendation for community members.The experimental results show that the proposed algo⁃rithm is significantly better than other traditional recommendation algorithms in terms of accuracy,recall,and F1.

关 键 词：形式概念分析 三支决策 因果力 推荐算法 网络形式背景 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]