《计算机应用研究》|Application Research of Computers

基于均衡接近度灰关联的增强二部图推荐算法

Enhanced bipartite graph recommendation algorithm based on grey correlational analysis by degree of balance and approach

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作者 张岐山,文闯
机构 福州大学 经济与管理学院,福州 350108
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2020)09-011-2620-05
DOI 10.19734/j.issn.1001-3695.2019.04.0114
摘要 协同过滤推荐算法的数据稀疏性与冷启动问题影响和制约了推荐的质量,传统用户—项目二部图信任和相似度计算受局部个别点关联因素的消极影响。首先提出一种基于均衡接近度灰关联方法计算项目流行度的二部图信任推荐,在此基础上提出用户偏好的增强二部图直接信任度机制,然后通过JMSD相关系数作为全局信任推荐。在MovieLens数据集下的对比实验表明,与基准算法对比改进的算法模型具有更低的平均绝对误差(MAE),提高了推荐质量,改善了冷启动问题。
关键词 协同过滤; 稀疏性; 冷启动; 二部图; 均衡接近度; 灰关联分析; 用户偏好
基金项目 国家自然科学基金青年基金资助项目(61300104)
福建省自然科学基金资助项目(2018J01791)
本文URL http://www.arocmag.com/article/01-2020-09-011.html
英文标题 Enhanced bipartite graph recommendation algorithm based on grey correlational analysis by degree of balance and approach
作者英文名 Zhang Qishan, Wen Chuang
机构英文名 School of Economics & Management,Fuzhou University,Fuzhou 350108,China
英文摘要 The data sparsity and cold start problems of the collaborative filtering recommendation algorithm affect the quality of the recommendation. Traditional user-item bipartite graph trust and the similarity calculation is negatively affected by the local individual point correlation factors. Firstly, this paper proposed a bipartite graph trust recommendation based on project popularity calculated by a grey correlational analysis by degree of balance and approach. On this basis, it proposed the user's pre-ference enhanced bipartite graph direct trust degree mechanism, and used the JMSD correlation coefficient as the global trust recommendation. The comparison experiments under the MovieLens dataset show that the improved algorithm has a lower mean absolute error(MAE) compared with the benchmark algorithm, it improves the recommendation quality and alleviates the cold start problem.
英文关键词 collaborative filtering; sparseness; cold start; bipartite graph; degree of balance and approach; grey correlational analysis; user preferences
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收稿日期 2019/4/19
修回日期 2019/7/4
页码 2620-2624
中图分类号 TP301.6
文献标志码 A