[1]李玲,王移芝.融合信息熵和加权相似度的协同过滤算法研究[J].计算机技术与发展,2018,28(05):23-26.[doi:10.3969/j.issn.1673-629X.2018.05.006]
　LI Ling,WANG Yi-zhi. Research on Collaborative Filtering Algorithm Based on Information Entropy and Weighted－similarity[J].,2018,28(05):23-26.[doi:10.3969/j.issn.1673-629X.2018.05.006]
点击复制

融合信息熵和加权相似度的协同过滤算法研究()

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

卷:
28
期数:
2018年05期
页码:
23-26
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
 Research on Collaborative Filtering Algorithm Based on Information Entropy and Weighted－similarity
文章编号:
1673-629X(2018)05-0023-04
作者:
李玲; 王移芝
北京交通大学 计算机与信息技术学院，北京 100044
Author(s):
LI Ling; WANG Yi-zhi
School of Computer and Information Technology，Beijing Jiaotong University，Beijing 100044，China
关键词:
协同过滤; 稀疏性; 差异信息熵; 加权相似度
Keywords:
collaborative filtering; sparsity; difference information entropy; weighted-similarity
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-629X.2018.05.006
文献标志码:
A
摘要:
针对传统协同过滤算法存在的稀疏性问题以及只考虑用户间共同评分项目数量而忽略具体评分值的问题,提出了融合信息熵和加权相似度的算法模型。该模型在信息熵的基础上引入了差异信息熵，即考虑到用户对共同评分项目的具体评分值的影响，有效缓解了由于原始评分数据稀疏而造成的推荐质量欠佳问题。另外在此基础上,通过引入调节因子将传统的相似度计量方法和差异信息熵通过加权平均,使用加权相似度计算用户之间的相似性,获得更好的近邻,提高了发现邻居的精确度。最后将该算法应用于MovieLens 数据集,并与传统的协同过滤算法进行比较，结果表明,该算法具有更好的推荐效果，证明了该模型的有效性和可行性。
Abstract:
Aiming at the problem of sparsity and only considering the number of common items to score between users and ignoring the specific score in traditional collaborative filtering,we propose a new algorithm model based on information entropy and weighted similarity,where we introduce the difference information entropy based on information entropy,which mainly consider the influence of specific score to common items,effectively alleviating the poor recommendation equality caused by sparse original rating matrix.In addition,by introducing the adjusted factor for weighted average for traditional similarity measurement method and the difference information entropy,we apply the weighted similarity to compute the similarity between the users and get better neighbors,to improve the precision of the neighbors.Last,it is applied to MovieLens dataset and compared with traditional collaborative filtering algorithm,and the experiments show that we can get better recommendation results,proving its effectiveness and feasibility．

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,(05):67.
[2]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,(05):69.
[3]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,(05):18.
[4]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,(05):70.
[5]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,(05):73.
[6]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,(05):210.
[7]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,(05):73.
[8]唐燕,张玲华.一种自适应数字助听器声反馈消除算法[J].计算机技术与发展,2013,(05):209.
　TANG Yan,ZHANG Ling-hua.An Algorithm for Adaptive and Digital Hearing Aid Acoustic Feedback Cancellation[J].,2013,(05):209.
[9]李克潮,蓝冬梅.一种属性和评分的协同过滤混合推荐算法[J].计算机技术与发展,2013,(07):116.
　LI Ke-chao,LAN Dong-mei.A Collaborative Filtering Hybrid Recommendation Algorithm for Attribute and Rating[J].,2013,(05):116.
[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,(05):66.
[11]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(05):42.
[12]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(05):71.
[13]蒋宗礼,王威,陆晨. 基于均值预估的协同过滤推荐算法改进[J].计算机技术与发展,2017,27(05):1.
　JIANG Zong-li,WANG Wei,LU Chen. 基于均值预估的协同过滤推荐算法改进[J].,2017,27(05):1.
[14]范 莹,郝琳娜,易 华,等.基于最大期望和协同过滤算法的研究与应用[J].计算机技术与发展,2017,27(12):139.[doi:10.3969/ j. issn.1673-629X.2017.12.030]
　FAN Ying,HAO Lin-na,YI Hua,et al.Research and Application of Algorithm Based on Maximum Expectation and Collaborative Filtering[J].,2017,27(05):139.[doi:10.3969/ j. issn.1673-629X.2017.12.030]
[15]余相,陈亮,胡亚兰,等.融合“S冶型相似度和关联度的协同过滤算法[J].计算机技术与发展,2019,29(03):35.[doi:10.3969/ j. issn.1673-629X.2019.03.007]
　YU Xiang,CHEN Liang,HU Ya-lan,et al.Collaborative Filtering Algorithm Combining “S” Similarity and Relation[J].,2019,29(05):35.[doi:10.3969/ j. issn.1673-629X.2019.03.007]

更新日期/Last Update: 2018-06-26