[1]樊艳清,梁宏宇,纪佳琪.协同过滤算法中相似度计算问题研究[J].计算机技术与发展,2020,30(08):91-96.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 015]
　FAN Yan-qing,LIANG Hong-yu,JI Jia-qi.Research on Similarity Calculation in Collaborative Filtering Algorithm[J].,2020,30(08):91-96.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 015]
点击复制

协同过滤算法中相似度计算问题研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年08期
页码:
91-96
栏目:
智能、算法、系统工程
出版日期:
2020-08-10

文章信息/Info

Title:
Research on Similarity Calculation in Collaborative Filtering Algorithm
文章编号:
1673-629X(2020)08-0091-06
作者:
樊艳清1; 梁宏宇2; 纪佳琪2
1. 河北民族师范学院 信息中心,河北 承德 067000; 2. 河北民族师范学院 数学与计算机科学学院,河北 承德 067000
Author(s):
FAN Yan-qing1; LIANG Hong-yu2; JI Jia-qi2
1. Information Center,Hebei Normal University for Nationalities,Chengde 067000,China; 2. School of Math and Computer Science,Hebei Normal University for Nationalities,Chengde 067000,China
关键词:
协同过滤; 推荐系统; 相似度; 评分矩阵; 大数据
Keywords:
collaborative filtering; recommendation system; similarity; rating matrix; big data
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 08. 015
摘要:
大数据时代由于信息过载问题使人们无法在海量数据中快速精准地获取有效信息。 为了解决个性化信息呈现问题,推荐系统应运而生。 在众多推荐算法中,协同过滤通过分析用户的历史行为信息,能够得到良好的推荐结果,成为推荐系统中使用最普遍的算法。协同过滤算法中相似度的计算方式直接影响着推荐结果。 针对目前缺乏综合评价不同相似度对推荐结果评价指标影响的相关研究,深入全面分析研究了余弦相似度、调整的余弦相似度、皮尔森相关系数、欧几里德相似度、谷本系数 5 种不同相似度的计算方法,并分析了对准确率、召回率、平均调和数和覆盖率 4 种不同评价指标的影响。 通过在真实数据集上的实验结果,给出了不同相似度计算方式的适用场景和优缺点。
Abstract:
In the era of big data, people cannot get effective information quickly and accurately in massive data due to information overload. In order to solve the problem of personalized information presentation,the recommender system emerge at the historic moment. Among different recommendation algorithms,collaborative filtering can obtain ideal recommendation results by analyzing the historical behavior of users,making it the most commonly used algorithm in the recommendation system. The similarity calculation method of collaborative filtering algorithm directly affects the recommenda-tion results. A comprehensive research on five similarity calculation methods is carried out since it is lack of comprehensive evaluation on the effect of varied similarity calculation methods on recommendation results. Four different evaluating indicator such as precision,recall,harmonic mean and coverage are analyzed. With the experimental results on the real datasets,the application domains and scenarios,advantages and disadvantages of different similarity calculation methods are proposed.

相似文献/References:

[1]查文琴 梁昌勇 曹镭.基于用户聚类的协同过滤推荐方法[J].计算机技术与发展,2009,(06):69.
　ZHA Wen-qin,LIANG Chang-yong,CAO Lei.Collaborative Filtering Recommendation Method Based on Clustering of Users[J].,2009,(08):69.
[2]曹毅 贺卫红.基于内容过滤的电子商务推荐系统研究[J].计算机技术与发展,2009,(06):182.
　CAO Yi,HE Wei-hong.Research on E- Commerce Recommender System Based on Content - Based Filtering[J].,2009,(08):182.
[3]赵鹏 蔡庆生 王清毅.一种用于文章推荐系统中的用户模型表示方法[J].计算机技术与发展,2007,(01):4.
　ZHAO Peng,CAI Qing-sheng,WANG Qing-yi.A Novel Representation of User Profile in Document Recommendation System[J].,2007,(08):4.
[4]高静 应吉康.基于人工免疫系统的推荐系统[J].计算机技术与发展,2007,(05):180.
　GAO Jing,YING Ji-kang.A Recommendation System Based on Artificial Immune System[J].,2007,(08):180.
[5]姜雅倩 王直杰 张珏.基于供求关系及协同过滤技术的推荐模型研究[J].计算机技术与发展,2007,(06):18.
　JIANG Ya-qian,WANG Zhi-jie,ZHANG Jue.Research on Recommendation Model Based on Supply and Demand Relation and Collaborative Filtering[J].,2007,(08):18.
[6]徐红 彭黎 郭艾寅 徐云剑.基于用户多兴趣的协同过滤策略改进研究[J].计算机技术与发展,2011,(04):73.
　XU Hong,PENG Li,GUO Ai-yin,et al.User-Based Collaborative Filtering Strategies More Interested in Improvement of Research[J].,2011,(08):73.
[7]杨东风 牛永洁.基于混合规则的图书推荐模型设计与研究[J].计算机技术与发展,2011,(07):210.
　YANG Dong-feng,NIU Yong-jie.Books Recommended Model Design and Research Based on Mixing Rules[J].,2011,(08):210.
[8]华铨平.基于FNN的家纺产品个性化推荐系统的研究[J].计算机技术与发展,2011,(09):183.
　HUA Quan-ping.Research on Personalized Recommendation System of Textile Products Used by Family Based on FNN[J].,2011,(08):183.
[9]吴月萍 王娜 马良.基于蚁群算法的协同过滤推荐系统的研究[J].计算机技术与发展,2011,(10):73.
　WU Yue-ping,WANG Na,MA Liang.Research of Collaboration Filtering Recommendation System Based on Ant Algorithm[J].,2011,(08):73.
[10]马言春 彭志平.基于市场机制的云服务管理研究[J].计算机技术与发展,2012,(03):214.
　MA Yan-chun,PENG Zhi-ping.Research on Cloud Service Management Based on Market Mechanism[J].,2012,(08):214.
[11]邵延振 蒙韧 袁鼎荣 李新友.基于Web结构分区的协同过滤推荐算法研究[J].计算机技术与发展,2010,(06):67.
　SHAO Yan-zhen,MENG Ren,YUAN Ding-rong,et al.Collaborative Filtering Recommendation Algorithm Research Based on Web Blocks[J].,2010,(08):67.
[12]游文 叶水生.电子商务推荐系统中的协同过滤推荐[J].计算机技术与发展,2006,(09):70.
　YOU Wen,YE Shui-sheng.A Survey of Collaborative Filtering Algorithm Applied in E- commerce Recommender System[J].,2006,(08):70.
[13]范虎,花伟伟.协同过滤推荐算法的研究与改进[J].计算机技术与发展,2013,(09):66.
　FAN Hu[],HUA Wei-wei[].Research and Improvement of Collaborative Filtering Recommendation Algorithm[J].,2013,(08):66.
[14]李荟,谢强,丁秋林. 一种基于情景的协同过滤推荐算法[J].计算机技术与发展,2014,24(10):42.
　LI Hui,XIEQiang,DING Qiu-lin. A Collaborative Filtering Recommendation Algorithm Based on Scenario[J].,2014,24(08):42.
[15]程高伟,丁亦喆,吴振强. 结合用户评分和项目标签的协同过滤算法[J].计算机技术与发展,2015,25(03):71.
　CHENG Gao-wei,DING Yi-zhe,WU Zhen-qiang. Collaborative Filtering Algorithm Combined User Ratings with Item Tags[J].,2015,25(08):71.
[16]张晓琳,付英姿,褚培肖. 杰卡德相似系数在推荐系统中的应用[J].计算机技术与发展,2015,25(04):158.
　ZHANG Xiao-lin,FU Ying-zi,CHU Pei-xiao. Application of Jaccard Similarity Coefficient in Recommender System[J].,2015,25(08):158.
[17]周莹莹,王晓军. 利用离群点算法预处理协同过滤推荐系统数据[J].计算机技术与发展,2015,25(09):129.
　ZHOU Ying-ying,WANG Xiao-jun. Pre-filtering Data of Collaborative Filtering Recommendation System by Outliers Algorithm[J].,2015,25(08):129.
[18]陈宗言,颜俊. 基于稀疏数据预处理的协同过滤推荐算法[J].计算机技术与发展,2016,26(07):59.
　CHEN Zong-yan,YAN Jun. Collaborative Filtering Recommendation Algorithm Based on Sparse Data Pre-processing[J].,2016,26(08):59.
[19]张天杰,曹苏燕,闫世洋. 基于项目近邻的约束概率矩阵分解算法[J].计算机技术与发展,2016,26(10):64.
　ZHANG Tian-jie,CAO Su-yan,YAN Shi-yang. A Constrained Probabilistic Matrix Factorization Algorithm Based on Item-neighborhood[J].,2016,26(08):64.
[20]张应辉,司彩霞. 基于用户偏好和项目特征的协同过滤推荐算法[J].计算机技术与发展,2017,27(01):16.
　ZHANG Ying-hui,SI Cai-xia. A Collaborative Filtering Algorithm Based on Interest of User and Attributes of Item[J].,2017,27(08):16.

更新日期/Last Update: 2020-08-10