[1]施海鹰. 基于关联规则挖掘的分类随机游走算法[J].计算机技术与发展,2017,27(09):1-11.
　SHI Hai-ying. Random-walk Classification Algorithm with Association Rules Mining[J].,2017,27(09):1-11.
点击复制

 基于关联规则挖掘的分类随机游走算法()
分享到：

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

卷:
27
期数:
2017年09期
页码:
1-11
栏目:
智能、算法、系统工程
出版日期:
2017-09-10

文章信息/Info

Title:
 Random-walk Classification Algorithm with Association Rules Mining
文章编号:
1673-629X（2017)09-0007-05
作者:
 施海鹰
 上海大学 计算机工程与科学学院
Author(s):
 SHI Hai-ying
关键词:
 ; 推荐系统; 关联规则; 分类随机游走算法; 信息过载
Keywords:
 ; recommendation system; association rules; categorical random-walk; information overload
分类号:
TP301.6
文献标志码:
A
摘要:
 随着互联网技术的不断进步和互联网的飞速发展,人们可以很方便地在互联网上寻找各种各样的信息.用户在寻找他们真正感兴趣的信息时会花费大量的时间,从而导致效率不高,这种现象被称作"信息过载".推荐系统是解决信息过载问题的一种行之有效的方法.目前,推荐系统中应用最广泛的两种推荐技术是基于内容的推荐算法和协同过滤推荐算法,但其不能很好地处理冷启动和稀疏性问题.为了更好地解决这两个问题,在对传统分类随机游走算法进行改进的基础上,提出了基于关联规则挖掘的分类随机游走算法.该算法利用关联规则挖掘的特性,挖掘用户属性与项目之间的关联,为新用户构造初始的评分向量,弥补了经典算法的不足,较好地处理了冷启动问题.验证实验结果表明,该算法具有较好的有效性和精确性.
Abstract:
 Along with the continuous progress of Internet technology and the rapid development of the Internet,people can easily find all kinds of information on the Internet. Users would spend a lot of time to search for information what they are really interested in,which is inefficient. The phenomenon is called information overload which is solved effectively by recommendation system as an effective method. However,the two most popular recommendation technologies in the current recommendation system are content-based recommendation and collaborative filtering recommendation, which cannot handle the problems of cold start and sparsity well. In order to better solve them,categorical random-walk algorithm based on association rules is proposed,which uses association rules to mine the association be-tween user attributes and items and constructs the initial score vectors for new users. It has made up for the shortage of the classic algo-rithm and better handles the cold start problem. The results of experiments prove its effectiveness and accuracy.

相似文献/References:

[1]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,(09):67.
[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,(09):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,(09):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,(09):180.
[5]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,(09):70.
[6]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,(09):183.
[7]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,(09):214.
[8]刘厚良.网络协同戏剧中个性化戏剧资源推荐系统[J].计算机技术与发展,2012,(08):25.
　LIU Hou-liang.Personalized Drama Resource Recommendation System in Cooperative Play[J].,2012,(09):25.
[9]陆晓敏 崇志宏 陈国庆.基于本体的商品推荐方法[J].计算机技术与发展,2012,(10):10.
　LU Xiao-min,CHONG Zhi-hong,CHEN Guo-qing.Product Recommending Method Based on Ontology[J].,2012,(09):10.
[10]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,(09):66.
[11]于海平[],林晓丽[],刘会超[]. 基于数据挖掘的移动广告个性化推荐研究[J].计算机技术与发展,2014,24(08):234.
　YU Hai-ping[],LIN Xiao-li[],LIU Hui-chao[]. Research of Mobile Internet Advertising Personalized Recommendation Based on Data Mining[J].,2014,24(09):234.
[12]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(09):42.
[13]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(09):71.
[14]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(09):158.
[15]徐新瑞,孟彩霞,周雯,等. 一种基于Spark时效化协同过滤推荐算法[J].计算机技术与发展,2015,25(06):48.
　XU Xin-rui,MENG Cai-xia,ZHOU Wen,et al. A Real-time Collaborative Filtering Recommendation Algorithm Based on Spark[J].,2015,25(09):48.
[16]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(09):129.
[17]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(09):59.
[18]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(09):64.
[19]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(09):16.
[20]张龙昌,张洪锐. 数字资源云服务推荐系统设计[J].计算机技术与发展,2017,27(08):139.
　ZHANG Long-chang,ZHANG Hong-rui. Design of Recommender System with Cloud Services of Digital Resource[J].,2017,27(09):139.

更新日期/Last Update: 2017-10-19