摘要
【目的】利用微博发布者和目标用户的多维信任关系对传统的微博推荐方法进行改进,以获得更好的推荐效果。【方法】通过将微博发布者和目标用户的相似信任度、熟悉信任度和影响力信任度线性调和,得到二者间的综合信任度,将其作为调整因子对基于内容的微博推荐方法进行改进。【结果】在真实数据上的实验结果表明,与传统的微博推荐方法相比,改进方法在F值和DCG值上均有一定程度提高。【局限】仅考虑相邻用户间的直接关系,未考虑不相邻用户间的间接关系。【结论】利用多维信任度改进传统微博推荐方法,可以提高推荐效果。
[Objective] This paper tries to improve microblog recommendation method with the trust relationship between microblog profiles and target users, aiming to improve the recommendation results. [Methods] First, the comprehensive trust between microblog users and target users is calculated by using the linear harmonic function of similarity, familiarity and influence. Then, the comprehensive trust degree is used as the adjustment factor to improve the content-based recommendation method. [Results] The F-Measure and DCG-Measure of the method was higher than those of the traditional ones. [Limitations] This method did not examine the indirect relationship among the non-adjacent users. [Conclusions] The proposed method could more effectively recommend microblogs.
作者
韩康康
徐建民
张彬
Han Kangkang;Xu Jianmin;Zhang Bin(School of Cyberspace Security and Computer,Hebei University,Baoding 071002,China;School of Management,Hebei University,Baoding 071002,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第12期95-104,共10页
Data Analysis and Knowledge Discovery
基金
国家社会科学基金后期资助项目“基于术语关系的贝叶斯网络检索模型扩展”(项目编号:17FTQ002)
河北省自然科学基金项目“基于贝叶斯网络的话题识别与追踪方法研究”(项目编号:F2015201142)的研究成果之一。
关键词
微博推荐
相似信任度
熟悉信任度
影响力信任度
Microblog Recommendation
Similarity Trust
Familiarity Trust
Influence Trust
