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

一种改进项目多属性类别划分的推荐算法

Recommendation algorithm for improving project multi-attribute classification

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作者 邱宁佳,薛丽娇,贺金彪,王鹏,杨华民
机构 长春理工大学 计算机科学技术学院,吉林 长春 130022
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文章编号 1001-3695(2020)10-010-2932-05
DOI 10.19734/j.issn.1001-3695.2019.06.0199
摘要 针对传统度量相似性方法中忽略项目多属性类别差异的问题,提出一种改进项目多属性类别划分的推荐算法。首先构建项目—用户隶属矩阵挖掘用户间的隶属关系,并创建相似邻居FP-Tree以提取最近邻居集;然后分析用户间共同项目相似性和项目多属性类别划分的差异性,通过权重因子将共同项目和多属性类别融合,构建CNB度量模型度量邻居相似程度;最后对所得相似用户进行降序排列,获取更加精准的相似用户,完成推荐工作。结合医药数据集验证该算法的有效性,结果表明其时间复杂度、推荐准确性和平均精度均值均有较好的提升。
关键词 隶属矩阵; FP-Tree; 多属性类别; CNB模型
基金项目 吉林省科技发展计划技术攻关项目(20190302118GX):吉林省教育厅“十三五”科学技术项目(JJKH20190600KJ)
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英文标题 Recommendation algorithm for improving project multi-attribute classification
作者英文名 Qiu Ningjia, Xue Lijiao, He Jinbiao, Wang Peng, Yang Huamin
机构英文名 College of Computer Science & Technology,Changchun University of Science & Technology,Changchun Jilin 130022,China
英文摘要 The traditional measurement method of similarity ignores the project of multi-attribute category differences. To avoid this problem, this paper proposed a recommendation algorithm for improving project multi-attribute classification. Firstly, it used the project-user membership matrix to explore the affiliation and created a similar neighbor FP-Tree to extract the nearest neighbor set. Then it analyzed the common item similarity between users and the difference of the project multi-attribute classification, and used the weight factor to combine the common project with multi-attribute classification. It constructed the CNB model to measure the similarity degree of neighbors. Finally, it sorted the similar users in descending order to obtain more accurate similar users and complete the recommendation work. In virtue of the medical dataset, it verified the effectiveness of the proposed algorithm. The experimental results show that the recommendation accuracy of time complexity and mean of average accuracy have been improved.
英文关键词 membership matrix; FP-Tree; multi-attribute categories; cos naive Bayes model
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收稿日期 2019/6/10
修回日期 2019/7/23
页码 2932-2936
中图分类号 TP391
文献标志码 A