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:2019,28(7):162-168
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基于有效特征子集提取的高效推荐算法
(1.青岛科技大学 信息科学与技术学院, 青岛 266061;2.山东科技大学 山东省智慧矿山信息技术重点实验室, 青岛 266590;3.山东建筑大学, 济南 250101;4.山东信息职业技术学院 软件系, 潍坊 261061)
Efficient Recommendation Algorithm Based on Feature Subset Extraction
(1.School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China;2.Shandong Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, China;3.Shandong Jianzhu University, Jinan 250101, China;4.Software Engineering Department, Shandong College of Information Technology, Weifang 261061, China)
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投稿时间:2019-01-16    修订日期:2019-02-03
中文摘要: 推荐系统是根据用户的历史信息对未知信息进行预测.用户项目评分矩阵的稀疏性是目前推荐系统面临的主要瓶颈之一.跨域推荐系统是解决数据稀疏性问题的一种有效方法.本文提出了基于有效特征子集选取的高效推荐算法(FSERA),FSERA是提取辅助域的子集信息,来扩展目标域数据,从而对目标域进行协同过滤推荐.本文采用K-means聚类算法将辅助域的数据进行提取来降低冗余和噪声,获取了辅助域的有效子集,不仅降低了算法复杂度,而且扩展了目标域数据,提高了推荐精度.实验表明,此方法比传统的方法有更高的推荐精度.
中文关键词: 跨领域  特征选择  聚类  协同过滤
Abstract:The recommendation system predicts the unknown information according to the user's historical information. Sparsity of user item scoring matrix is one of the main bottlenecks faced by recommendation system. Cross-domain recommendation system is an effective method to solve the problem of data sparsity. In this study, an Efficient Recommendation Algorithm based on effective Feature Subset selection (FSERA) is proposed. FSERA extracts subset information of auxiliary domain to expand target domain data, so as to collaboratively filter recommendation for target domain. In this study, K-means clustering algorithm is used to extract data from the auxiliary domain to reduce redundancy and noise, and to obtain an effective subset of the auxiliary domain, which not only reduces the complexity of the algorithm, but also expands the target domain data and improves the recommendation accuracy. Experiments show that this method has higher recommendation accuracy than traditional methods.
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基金项目:国家自然科学基金(61402246,61503220);山东省自然科学基金(ZR2019MF014,ZR2017BF023);光电技术与智能控制教育部重点实验室(兰州交通大学)开放课题基金(KFKT2018-2)
引用文本:
于旭,王前龙,徐凌伟,田甜,徐其江,崔焕庆.基于有效特征子集提取的高效推荐算法.计算机系统应用,2019,28(7):162-168
YU Xu,WANG Qian-Long,XU Ling-Wei,TIAN Tian,XU Qi-Jiang,CUI Huan-Qing.Efficient Recommendation Algorithm Based on Feature Subset Extraction.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):162-168

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