基于弱概念相似度的组推荐方法  

Group Recommendation Method Based on Weaken‑Concept Similarity

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

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

出　　处：《数据采集与处理》2023年第2期439-450,共12页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(11971211,12171388)。

摘　　要：网络数据下的概念认知与知识发现是网络背景下机器学习和人工智能的重要研究方向,已被引入到推荐系统研究中。现有的基于概念格的推荐方法忽视了节点之间的网络结构关系,同时构造概念格的效率低且构建概念集合的约束条件较严,在大规模的社交网络中难以实现。为解决这些问题,本文在网络形式背景的框架下,综合复杂网络的拓扑结构和弱概念相似度,提出了基于弱概念相似度的组推荐算法。首先,定义属性度、属性密度来描述属性的重要性,通过改进的节点影响力来确定专家节点;其次,利用专家节点划分社区,在划分的社区中通过属性弱概念下限相似度进行组推荐研究,进而获取推荐规则并对相应社区进行组推荐;最后,利用MovieLens数据集和Filmtrust数据集分析了各参数对本文所提算法的影响,并确定了参数的合理取值。将本文所提算法与其他推荐算法进行比较测试,实验验证了本文算法的有效性。Concept cognition and knowledge discovery from network data are important research directions of machine learning and artificial intelligence under the network background,and have been introduced into the study of recommendation system.The existing recommendation methods based on concept lattice ignore the network structure relationship between nodes.At the same time,the efficiency of constructing concept lattice is low and the constraints of constructing concept set are strict,which is difficult to realize in large-scale social networks.In order to solve these problems,this paper integrates the topology of complex networks and weaken-concept similarity under the framework of network formal context,and proposes a group recommendation algorithm based on weaken-concept similarity.Firstly,the importance of attributes is described by defining attribute degree and attribute density,and then the expert nodes are determined by using the improved node influence.Secondly,the community is divided by expert nodes,the group recommendation research is carried out by using the lower limit similarity of attribute weaken-concept in the divided community,and then the recommendation rules are obtained and the group recommendation is applied to the corresponding communities.Finally,the influence of various parameters on the algorithm is analyzed on MovieLens and Filmtrust datasets,and reasonable values of the parameters are determined.After that,the proposed algorithm is compared with other recommended algorithms,and the experiments show that the proposed algorithm is effective.

关 键 词：形式概念分析 网络形式背景 节点影响力 弱概念相似度 组推荐 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]