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

结合评分比例因子及项目属性的协同过滤算法

Collaborative filtering algorithm combined with score scale factor and item attribute

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作者 李淑芝,李志军,邓小鸿
机构 江西理工大学 a.信息工程学院;b.应用科学学院,江西 赣州 341000
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文章编号 1001-3695(2020)03-009-0680-04
DOI 10.19734/j.issn.1001-3695.2018.08.0627
摘要 针对传统的协同过滤算法存在用户评分矩阵稀疏及未考虑项目属性之间关系的问题,提出了结合评分比例因子及项目属性的协同过滤算法。首先利用评分矩阵得出项目之间的共同与非共同评分用户数量比矩阵,以此增加项目共同评分用户的影响度,减少用户—项目评分矩阵的稀疏性对项目相似度计算带来的误差;然后对项目属性量化得出其对项目相似度的影响权重,提高项目相似度计算的准确性,根据以上两点提出了一种结合评分比例因子及项目属性权重作为项目相似度权重的算法。实验结果表明,该算法在召回率和准确率上相比现有的方法分别提高了5.1%和4.7%,适用于电商类网站的个性化推荐。
关键词 协同过滤; 稀疏矩阵; 评分比例因子; 项目属性
基金项目 国家自然科学基金资助项目(61762046)
本文URL http://www.arocmag.com/article/01-2020-03-009.html
英文标题 Collaborative filtering algorithm combined with score scale factor and item attribute
作者英文名 Li Shuzhi, Li Zhijun, Deng Xiaohong
机构英文名 a.College of Information Engineering,b.College of Applied Science,Jiangxi University of Science & Technology,Ganzhou Jiangxi 341000,China
英文摘要 There exists several issues in traditional collaborative filtering algorithms, which has the sparsity of user rating matrix and ignores the relationship between item attributes. Considering above problems, this paper proposed a novel collaborative filtering algorithm combining score ratio factor and item attribute. This algorithm used the scoring matrix to obtain the ratio matrix of common and non-common score users between items. Therefore, it increased the influence degree of the users of the item common score, and reduced the error caused by the sparsity of the user-item scoring matrix on the item similarity calculation. It quantified the item attribute to obtain the weight of the item similarity, and it also improved the accuracy of the item similarity calculation. According to the above two points, this paper proposed an algorithm combining scoring scale factor and item attribute weight as item similarity weight. Experimental results show that the proposed algorithm improves the recall rate and accuracy by 5.1% and 4.7% respectively compared with the existing methods. The algorithm is suitable for personalized recommendation of e-commerce websites.
英文关键词 collaborative filtering; sparse matrix; scoring scale factor; item attribute
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收稿日期 2018/8/24
修回日期 2018/10/16
页码 680-683
中图分类号 TP301.6
文献标志码 A