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

融合协同过滤的线性回归推荐算法

Linear regression recommendation algorithm with collaborative filtering

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作者 庞海龙,赵辉,李万龙,马莹,崔岩
机构 长春工业大学 计算机科学与工程学院,长春 130012
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文章编号 1001-3695(2019)05-005-1302-03
DOI 10.19734/j.issn.1001-3695.2017.11.0732
摘要 针对传统协同过滤算法中存在数据稀疏性问题,提出融合协同过滤的线性回归推荐算法。根据用户对项目的评分以及用户和项目的自身特征,构建用户间和项目间相似矩阵。基于相似矩阵,选出用户和项目最近邻集合,分别通过基于用户和基于项目的协同过滤算法来预测用户已评分项目的评分,将预测评分与真实评分的差值作为特征,组合在一起生成新的训练数据。把新的训练数据作为线性回归模型的输入,根据训练好的模型预测未知评分,采用top-N算法产生推荐列表。在MovieLens数据集上进行实验,实验结果表明新算法的推荐准确性较传统协同过滤算法有显著提高。
关键词 线性回归; 协同过滤; 相似性; 推荐算法
基金项目 国家自然科学基金资助项目(61472049)
吉林省教育厅“十二五”科学技术研究项目(2014132)
本文URL http://www.arocmag.com/article/01-2019-05-005.html
英文标题 Linear regression recommendation algorithm with collaborative filtering
作者英文名 Pang Hailong, Zhao Hui, Li Wanlong, Ma Ying, Cui Yan
机构英文名 School of Computer Science & Engineering,Changchun University of Technology,Changchun 130012,China
英文摘要 This paper proposed a linear regression algorithm to integrate collaborative filtering based on the data sparse influence of the traditional collaborative filtering algorithm. Firstly, it built a similarity matrix between the user and the project based on the user's rating of the project, as well as the user and the project's own characteristics. Secondly, based on the similarity matrix, it selected the user and project nearest neighbor set. It predicted the score that the users had graded respectively by the way of collaborative filtering algorithms based on the user and the project. And it would take the difference between predicted scores and the real scores as features to generate new training data, and regarded the new training data as the input of the linear regression model. Finally, according to the training model, it could predict the unknown score, and used the top-N algorithm to generate the recommended list. It conducted the experiment on the MovieLens data set. The experimental result shows that the proposed accuracy of the new algorithm improves compared with the traditional collaborative filtering algorithm.
英文关键词 linear regression; collaborative filtering; similarity; recommendation algorithm
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收稿日期 2017/11/1
修回日期 2018/1/2
页码 1302-1304,1310
中图分类号 TP301.6
文献标志码 A