摘要
形式概念分析作为知识发现的方法,在理论分析和实际应用中已经取得很多成果。随着三维数据的涌现,许多学者开始了对三元形式概念分析的研究。但是,目前该领域的研究和应用较少,尤其还没有被应用到推荐系统。文中介绍了三元概念的构建及其社会化推荐应用。首先设计启发式信息,构造覆盖所有用户的三元概念集合,启发式信息旨在生成外延和内涵均有一定规模的强概念;然后根据拟推荐项目的属性来筛选用户合适的社会关系,并结合项目在概念中的流行度实现推荐预测。文中分别在真实数据集和抽样数据集中进行了3个实验。实验1对比了启发式方法和∨_(o c)运算构造的三元概念数量及其运行时间,其中∨_(o c)运算构造的概念数量少、耗时长且对推荐的提升效果不明显;实验2对比了推荐效果的精确度、召回率和F1值,揭示了增加条件可以有效提升推荐效果;实验3的结果表明,基于三元概念的推荐算法的推荐效果优于KNN及GRHC。
Formal concept analysis is a knowledge discovery method that has great achievements in theory and application.Recently,with the emergence of three-dimensional data,triadic formal concept analysis has been developed.However,there are few researches and applications in this field,especially it has not been applied to recommendation systems.This paper proposes an efficient triadic concept set construction method and applies it to social recommendation.Firstly,the heuristic information is designed to generate a set of triadic concepts covering all users.Heuristic information aims to construct strong concepts with a certain scale of extension and intension.Then,appropriate social relations are screened through the attributes of the proposed items,and the recommendation prediction is realized by combining the popularity of the items in the concept.Three experiments are carried out in real data set and sampled data set respectively.In the first experiment,the number of triadic concepts and running time constructed by the heuristic method and∨oc operation are compared respectively.The concepts constructed by the∨oc operation do not significantly improve the recommendation effect.The second experiment compares the accuracy,recall rate and F1 of the recommendation effect.It reveals that increasing the number of conditions can effectively improve the recommendation effect.The results of the last experiment show that the recommendation effect of the new algorithm is better than that of KNN and GRHC.
作者
刘忠慧
赵琦
邹璐
闵帆
LIU Zhong-hui;ZHAO Qi;ZOU Lu;MIN Fan(School of Computer Science,Southwest Petroleum University,Chengdu 610500,China;Institute for Artificial Intelligence,Southwest Petroleum University,Chengdu 610500,China)
出处
《计算机科学》
CSCD
北大核心
2021年第6期234-240,共7页
Computer Science
基金
国家自然科学基金(41604114)
四川省青年科技创新团队(2019JDTD0017)。
关键词
三元形式概念分析
启发式算法
项目条件
流行度
社会化推荐
Triadic formal concept analysis
Heuristic algorithm
Item conditions
Popularity
Social recommendation
