摘要
针对传统知识推荐算法存在的语义缺失和精准性低问题,提出一种基于改进LDA-FCM的知识推荐算法。首先获取用户知识文档,采用主题优化的LDA模型挖掘用户知识主题。继而通过FCM算法将用户聚类,缩小相似度计算的遍历范围,并采用JS散度代替欧氏距离,实现FCM对象到用户的转换。最后基于UserCF算法构建用户对知识的兴趣指数并进行TOP-N推荐。爬取中国知网500篇期刊论文实测发现,与传统UserCF算法相比,改进算法的准确率、召回率和F1值分别提高了22.35%、55.92%、49.06%。
The improved LDA topic model and FCM user clustering algorithm are integrated into UserCF to solve the problem of semantic missing and low precision of current knowledge recommendation algorithm.An adaptive function is constructed to achieve an objective solution of the number of topics and the knowledge topic of interest to the user is mined through LDA.FCM algorithm is used to cluster the user topics,and the users are divided into clusters with similar interests,thereby narrowing the traversal range of the user similarity calculation.The JS divergence is used instead of the Euclidean distance to convert FCM object to user.Finally,based on the UserCF algorithm,the user’s interest index on knowledge is constructed,and the user is recommended by TOP-N.The comparison results show that the precision,recall and F1 value of the proposed algorithm are increased by 22.35%,55.92%,49.06%.
作者
张建华
冉佳
刘柯
Zhang Jianhua;Ran Jia;Liu Ke(School of Management Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处
《科技管理研究》
CSSCI
北大核心
2020年第19期140-146,共7页
Science and Technology Management Research
基金
国家社会科学基金项目“隐性知识深度服务体系研究”(19BTQ035)。
关键词
LDA主题模型
知识推荐
协同过滤
模糊C均值
LDA topic model
knowledge recommendation
collaborative filtering
Fuzzy C-means